Client business models, process business risks and the risk of material misstatement of re

Description
This research tests for understanding of the theory of business risk auditing. Focusing on process-level
instead of entity-level business risk assessment, the study tests predictions for risk assessments given
two business strategies and the fundamental operating processes of a manufacturing firm. Risk assessments
for a client using a product differentiation strategy are compared with assessments made for a
client using an operational excellence strategy. The focus is on hypotheses for judgments of processspecific
business risk and the risk of material misstatement (RMM) of revenue. Business risk is the
risk that a process will not produce the level of effectiveness necessary to achieve one or more entitylevel
strategic objectives (Bell, Peecher, & Solomon, 2002). Graduate accounting students with auditing
experience demonstrated significant understanding of the predicted relationships. With a few exceptions,
they (1) produced process-specific business risk judgments that are positively associated with
RMM judgments for the critical processes of the product differentiation strategy, and not for the noncritical
processes; (2) generated process-specific judgments of business risk that mediated the association
of Production process performance and the RMM of revenue; and, (3) when the three product
generation processes were performing less well, correctly assessed the highest RMM of revenue

Client business models, process business risks and the risk of material
misstatement of revenue
*,**
William F. Wright
Department of Accountancy, College of Business, University of Illinois at Urbana-Champaign, 284 Wohlers Hall, MC-706, 1206 South Sixth Street,
Champaign, IL 61820, USA
a r t i c l e i n f o
Article history:
Received 15 August 2014
Received in revised form
10 November 2015
Accepted 19 November 2015
Available online xxx
Keywords:
Business risk auditing
Risk-based auditing
Risk assessment
Analytical procedures
Strategic management
Business models
a b s t r a c t
This research tests for understanding of the theory of business risk auditing. Focusing on process-level
instead of entity-level business risk assessment, the study tests predictions for risk assessments given
two business strategies and the fundamental operating processes of a manufacturing ?rm. Risk assess-
ments for a client using a product differentiation strategy are compared with assessments made for a
client using an operational excellence strategy. The focus is on hypotheses for judgments of process-
speci?c business risk and the risk of material misstatement (RMM) of revenue. Business risk is the
risk that a process will not produce the level of effectiveness necessary to achieve one or more entity-
level strategic objectives (Bell, Peecher, & Solomon, 2002). Graduate accounting students with audit-
ing experience demonstrated signi?cant understanding of the predicted relationships. With a few ex-
ceptions, they (1) produced process-speci?c business risk judgments that are positively associated with
RMM judgments for the critical processes of the product differentiation strategy, and not for the non-
critical processes; (2) generated process-speci?c judgments of business risk that mediated the associa-
tion of Production process performance and the RMM of revenue; and, (3) when the three product
generation processes were performing less well, correctly assessed the highest RMM of revenue. Using
this comprehensive set of conditions, contrary to many expressed concerns in the literature, the par-
ticipants indicate business risk and RMM judgments that re?ect signi?cant understanding of the sub-
tleties of business risk assessment.
© 2015 Elsevier Ltd. All rights reserved.
1. Introduction
A central premise of ?nancial auditing is that understanding of a
client's business strategy, strategic objectives and critical business
processes will provide evidence to evaluate management's ?nan-
cial statements (Bell, Peecher, & Solomon, 2005; Knechel, 2007;
Kochetova-Kozloski & Messier, 2011). Auditing standards empha-
size the importance of understanding of the business risks of a
client's business model during the reporting period (International
Federation of Accountants (IFAC), 2012; Public Company
Accounting Oversight Board (PCAOB), 2010). Understanding of a
client's business model permits an auditor to both comprehend
how operation of the business produces ?nancial results and to
assess the risk of their material misstatement (RMM) (PCAOB
2010).
1
Furthermore, PCAOB inspectors evaluate an auditor's un-
derstanding of the client's business model when they evaluate
audit judgments, e.g., auditors' testing of the book value of intan-
gible assets and how customer relationships relate to the recogni-
tion of revenue (PCAOB, 2014). Most important, inadequate
understanding of business risks can result in an audit failure (e.g.,
Erickson, Mayhew, & Felix, 2000).
*
I would like to thank the following individuals for their helpful comments on
this research: Andy Bauer, Tim Bauer, Paul Beck, Tim Brown, Brian Daugherty, Brent
Garza, Gary Hecht, Ira Solomon, and Adam Presslee as well as the attendees at my
presentation at the 2013 Auditing Section Midyear Conference.
**
Approval of this research was granted by the Institutional Review Board at the
University of Illinois.
E-mail address: [email protected].
1
Chesbrough and Rosenbloom (2002, 529e536; also see Huelsbeck et al., 2011)
de?ne six attributes of a ?rm's business model. Essentially, the six attributes
(underlined here) of management's reasoning include articulating a value propo-
sition for targeted customers relative to competitors' product offerings. In addition
to these strategic aspects, operating attributes include specifying key business
processes within the ?rm's value chain such that suitable pro?t potential exists
within a network of customers and suppliers.
Contents lists available at ScienceDirect
Accounting, Organizations and Society
j ournal homepage: www. el sevi er. com/ l ocat e/ aoshttp://dx.doi.org/10.1016/j.aos.2015.11.005
0361-3682/© 2015 Elsevier Ltd. All rights reserved.
Accounting, Organizations and Society 48 (2016) 43e55
While business risk auditing continues to be a central frame-
work for auditing (Kochetova-Kozloski, Kozloski, & Messier, 2013),
whether auditors can achieve the necessary in-depth understand-
ing of the business risks generated by different strategies and
business models remains unclear (Brewster, 2011; van Buuren,
Koch, van Nieuw Amerongen, & Wright, 2014; Knechel, Salterio,
& Kochetova-Kozloski, 2010; Peecher, Schwartz, & Solomon,
2007; Schultz, Bierstaker, & O'Donnell, 2010). For example, Schultz
et al. (2010) found that auditors incorporated their entity-level
business risk assessments into their RMM judgments only if they
were trained in the use of Strategic-SystemAuditing (SSA) methods
and received SSA formatted evidence instead of transaction-
formatted evidence. Moreover, while non?nancial measures of
process performance have predictive validity for detecting mis-
statements (Brazel, Jones, & Zimbelman, 2009), auditors have dif-
?culty using them during their judgment processes (Cohen,
Krishnamoorthy, & Wright, 2000; Trotman & Wright, 2012).
However, use of both types of evidence is necessary for an auditor
to construct a valid mental model of a client's business model
(Brewster, 2011; O'Donnell & Perkins, 2011). Having a valid mental
model is important because different business strategies create
different risks for generation of the economic events that are the
basis for ?nancial accounting balances (PCAOB, 2010). A valid
mental model also permits informed questioning of management's
assumptions and effective exercise of professional skepticism
(Nelson, 2009). However, researchers have asserted that auditors
may not correctly interpret the implications of different strategies
and business models for their business risk and RMM judgments
(van Buuren et al., 2014; Knechel, 2007; Peecher et al., 2007).
Incorrect or incomplete interpretations may jeopardize audit
quality because effective audit planning requires informed risk
assessments (PCAOB, 2013).
Strategic management theory and concepts (e.g., Mahoney &
Qian, 2013), especially regarding the design and operation of a
business model, provide the theoretical basis for business risk
auditing. A ?rm's business model includes a business strategy,
strategic objectives and business processes that implement the
strategy (Casadesus-Masanell & Ricart, 2010; Teece, 2010). Opera-
tion of the processes creates value for targeted customers; there-
fore, certain processes will be critical for achievement of the ?rm's
strategic objectives. For the critical processes, uncertainty
regarding achievement of the objectives results in business risk at
the process level. Building on the business model concept, auditing
theory adds the linkage between operation of the client's business
model and the client's ?nancial statement results, including their
possible misstatement (Bell, Marrs, Solomon, & Thomas, 1997; Bell
et al., 2002; Peecher et al., 2007).
The current research tests for understanding of the theory of
business risk auditing. I test the premise that informed graduate
students acting as surrogates for staff auditors will understand and
implement in their judgments the process risk implications of
different business strategies and business models. Research that
tests for understanding of the theory is important because the
existing literature indicates inconsistent results on auditors’ ability
to conduct an effective strategic analysis (see the next section).
My testing of the ability of the participants to provide judg-
ments consistent with the theory also extends process-level busi-
ness risk research in auditing (Ballou, Earley, & Rich, 2004;
Kochetova-Kozloski et al., 2013). The context is risk assessment
before tests of controls and during application of preliminary
analytical procedures. In this context, the participants simulta-
neously considered the risks of a manufacturing ?rm's primary
business processes (some of which are critical given the ?rm's
strategy), provided process-speci?c business risk assessments, and
assessed the relationship of process-speci?c business risks with the
RMM of revenue. The ?rst research issue is whether the partici-
pants will understand the process-speci?c business risk implica-
tions of an innovation-oriented, risky product differentiation
strategy relative to an operational excellence strategy based on cost
minimization and competing based on a lowproduct price. Use of a
product differentiation strategy, which requires constant, signi?-
cant product innovation and excellent customer service, results in
substantial business risk for revenue generation (e.g., Bentley,
Omer, & Sharp, 2013). Within-industry examples of product dif-
ferentiation versus operational excellence strategies are, respec-
tively, Apple versus Dell in the specialty electronics industry,
Nordstrom versus Walmart in retailing, and Starbucks versus
Dunkin’ Donuts for consumer food items. The results indicate, with
a few exceptions, that the participants provided the predicted
business risk magnitudes and associations of process-speci?c
business risks with the RMM of revenue for the product differen-
tiation strategy.
A second research issue, given the business risks for a client
pursuing a product differentiation strategy, is whether judgments
of process-speci?c business risk for critical processes will mediate
the relationship between levels of process performance and the
RMM of revenue. Recognition of mediation of this relationship re-
quires sophisticated understanding of why revenue could be mis-
stated given the process-speci?c business risks. Partially consistent
with expectations for three critical processes, the judgments of
process-speci?c business risk for the Production process mediated
the association of levels of process performance and the RMM of
revenue.
A third research issue is whether the participants will under-
stand the business risk implications of the performance of the
product generation processes for a manufacturing ?rm. Two pro-
?les of the performance of the three critical product generation
processes (New Product Development, Procurement and Produc-
tion) are included. The expectation is, for the client pursuing a
product differentiation strategy, when product generation perfor-
mance is relatively low and business risk is relatively high, the
participants will assign a higher RMM of revenue (Kumar &
Subramanian, 1998; Terziovski, 2010). In addition, when product
generation performance is relatively high, and business risk is
relatively low, no difference in the RMM of revenue for the two
strategies is expected. The participants were able to assess correctly
the RMM of revenue given the different conditions for relatively
low and high pro?les of product generation performance.
There are multiple contributions of this research. First, the
research provides evidence on the extent to which the participants
can implement the theory of business risk auditing, including the
linkages between business strategies, business models, assessment
of process-level business risks and the RMM of revenue. This is the
?rst study to compare results from two different client strategies.
Second, the study provides newinsights on the ability of auditors to
use non-?nancial information effectively to assess process-level
business risks (Ballou et al., 2004; Kochetova-Kozloski et al., 2013)
and relate them to the RMM of revenue. Process-speci?c business
risks are important because they can cause misstated statements;
they also cause entity-level business risks (e.g., Knechel, et al., 2010;
Kochetova-Kozloski & Messier, 2011; Schultz et al., 2010). Consid-
ered simultaneously are the process-level business risks for each of
the fundamental operating processes of a manufacturing client.
Third, while authors have suggested that auditors may encounter
dif?culty when they interpret top-down strategic aspects of busi-
ness models (e.g., van Buuren et al., 2014; Knechel, 2007; Power,
2007), the participants were able to distinguish between many of
the subtle and important business risk implications of the two
different types of business strategies.
W.F. Wright / Accounting, Organizations and Society 48 (2016) 43e55 44
2. Theory and hypotheses
2.1. Business risk analysis and the risk of material misstatement
assessments
Business risk assessment is a fundamental component of busi-
ness risk auditing (Allen, Hermanson, Kozloski, & Ramsey, 2006;
Knechel et al., 2010; Lemon, Tatum, & Turley, 2000; Schultz et al.
2010). Knechel et al. (2010, 317) note that “In general, business
risk auditing is characterized by a top-down focus on a client's
competitive environment, strategy for success, and critical internal
processes (references omitted)”. International Auditing Standard
315R requires evaluation of a client's strategy and objectives, and
focus on the generation and effects of business risk on the RMM
(IFAC, 2012). The PCAOB requires that auditors understand “The
company's objectives and strategies and those related business
risks that might reasonably be expected to result in risks of ma-
terial misstatement”, emphasis in the original (PCAOB, 2012, par.7).
To understand a client's business risks and the potential for state-
ment misstatement, auditors conduct strategic analysis, i.e.,
consideration of the strategic design and operation of the client's
business model (Knechel et al., 2010; Kochetova-Kozloski & Mes-
sier, 2011; Peecher et al. 2007).
Previous research reveals inconsistent results when auditors use
strategic analysis to assess entity-level business risks and the RMM
of ?nancial statements. Knechel et al. (2010) report that when au-
ditors completed an in-depth versus a super?cial strategic analysis,
they used more information for both business risk and RMM
judgments, especially when performance measures common to
both business units were benchmarked. Their in-depth strategic
analysis permitted them to develop a more complex and complete
mental model of a client's business and use more information.
Brewster (2011) reports that use of systems-oriented mental
models permitted more valid expectations, more ef?cient con-
struction of mental simulations and better understanding that an
inconsistent explanation was less credible. In contrast to these
positive ?ndings, Kochetova-Kozloski and Messier (2011) requested
that auditors performand document a detailed analysis of a client's
strategy. The auditors who completed the analysis did not identify
more business risks or ?nancial statement risks; however, they did
report RMM judgments that are more consistent with the mean
RMMgenerated by a panel of experts. O'Donnell and Schultz (2005)
report an interesting yet troublesome ?nding: auditors who
completed an assessment of a client's strategy before starting
analytical procedures biased their judgments of misstatement risk
at the account level. Judgments of the viability of the client's
strategy are positively associated with levels of account misstate-
ment risk when such an association was not expected or justi?ed,
resulting in a negative “halo effect.” In addition, given inconsistent
information, the auditors who made a strategic assessment
increased the account-level misstatement risk less than did audi-
tors who did not complete the strategic assessment.
Furthermore, whether there will be a bene?t of entity-level
strategic analysis can be dependent on the presentation of the
audit evidence. O'Donnell and Schultz (2003) report that auditors
presented with a business process organization of evidence versus
a transaction cycle organization identi?ed more of the seeded risks
of misstatement; they also assessed larger changes in the average
account-level RMM. Wright and Berger (2011) indicate that, when
management offered a fraudulent explanation for in?ated current
revenue, audit evidence organized based on the client's strategy
and objectives resulted in more valid fraud risk assessments than
did chronologically ordered evidence. However, Schultz et al.
(2010) found that both training in the use of Strategic-System
Auditing (SSA) methods and SSA formatted (versus transaction-
formatted) evidence were necessary for auditors to integrate their
entity-level business risk assessments into their overall RMM
judgments. O'Donnell and Perkins (2011) demonstrate the rele-
vance of a system representation of audit evidence. Auditors used
either a causal loop diagram that emphasized associations among
accounts or a process representation when they acquired infor-
mation about changes in account balances. When asked to identify
signi?cant ?uctuations, the users of the causal loop diagram
assigned higher relevance to information pertaining to seeded
inconsistent ?uctuations; however, account misstatement risk was
unaffected. When a second group of auditors was asked to explain
(versus identity) ?uctuations, the users of the causal loop diagram
assigned higher information relevance ratings and account level
misstatement risk for seeded inconsistent account balances. In
summary, the entity-level research indicates that auditors can
conduct strategic analysis effectively but the results are contingent
on both the context and the presentation of evidence.
Analysis of business risks at the process-level and their associ-
ation with the RMM of statement magnitudes is a different level of
analysis. Process-level business risks are more granular in their
effects and risks. Kochetova-Kozloski et al. (2013) found that, for
auditors who conducted a business risk analysis of the product/
service delivery process, the number of identi?ed process-level
business risks was signi?cantly associated with the number of
identi?ed entity-level business risks. In addition, identi?cation of
more signi?cant process-level business risks was associated with
higher levels of the RMM of the overall entity. Ballou et al. (2004)
found that the interpretation of the business risk of the logistics
and distribution process was contingent on whether the perfor-
mance of the ?rm was consistent with, or trailing, industry norms
(a form of benchmarking).
Process-speci?c business risks are important for RMM assess-
ment, especially for the processes that the client's strategy in-
dicates are critical for effective ?rm performance and have higher
business risk; the critical processes become critical success factors
(Bell et al., 2002).
2
However, relatively little is known about the
ability of auditors to make process-level business risk assessments.
The current research tests for understanding of the impact of crit-
ical business processes on the RMM of revenue.
2.2. Business model analysis and causal reasoning
Performing process-level strategic analysis and generating RMM
assessments involves causal reasoning for processes operating in a
client's value chain. Causal reasoning (Brewster, 2011; Peecher
et al., 2007; Vera-Mu~ noz, Shackell, & Buehner, 2007) is necessary
to determine which processes are critical for success of the ?rm's
strategy. Client management suggests elements of a ?rm's strategy
and strategic objectives using strategy maps (Cheng & Humphreys,
2012; Kaplan & Norton, 2004) and other business model repre-
sentations (Huelsbeck, Merchant, & Sandino, 2011). These repre-
sentations can assist auditors in their development of mental
models that capture the causal design of a client's business model
(Choy & King, 2005; Knechel et al., 2010; Kochetova-Kozloski &
Messier, 2011).
Current research in both cognitive science (Sloman & Lagnado,
2005) and psychology (Gigerenzer & Gaissmaier, 2011; Holyoak &
Cheng, 2011) indicates that auditors may be able to conduct
causal analysis effectively. Holyoak and Cheng (2011) describe a
2
Bell et al. (2002, 11) indicate that “A CSF is a business activity (or set of activ-
ities) that must be performed exceptionally well for the organization to attain its
strategic objectives. At the entity level business processes are CSFs because of their
importance to the attainment of strategic objectives.”
W.F. Wright / Accounting, Organizations and Society 48 (2016) 43e55 45
general framework for causal reasoning (also see Koonce, Seybert,
& Smith, 2011 and Srivastava, Mock, Pincus, & Wright, 2012). Us-
ing their three-part framework, auditors would apply their prior
knowledge of business models and client operations to construct
scenarios and reduce cognitive demands (Brewster, 2011). For
example, auditors would recognize the importance of the Customer
Service process for clients pursuing a product differentiation
strategy. In addition, auditors would interpret associations (co-
variations) between events, such as the performance of processes
and achieved levels of strategic objectives (Vera-Mu~ noz et al.,
2007). Finally, auditors would apply their awareness of the tem-
poral ordering of events. For example, lagged and current business
process performance and current ?nancial results (Holyoak &
Cheng, 2011). Koonce et al. (2011, 218) describe a similar para-
digm. Their “cues-to-causality” include covariation and meaningful
ordering of events, and similarity and contiguity of events.
Recent accounting studies provide evidence for effective causal
reasoning. Brewster (2011) reports that use of systems-oriented
mental models enabled more informed and ef?cient strategic
analysis (also see above). Cheng and Humphreys (2012) found that
accounting graduate students using causal linkages depicted by a
strategy map (Kaplan and Norton, 2004) generated better judg-
ments of strategy appropriateness and information relevance. In a
study of resource allocation, Vera-Mu~ noz et al. (2007) found that
auditors who used a causal model and received supportive
correlation-oriented benchmark data allocated more resources to
the optimal alternative for process improvement. Finally, Tayler
(2010) reports that MBA students using a causal chain versus four
categories of performance measures committed less motivated
reasoning (Kunda, 1990) when they were involved in the selection
of both a strategy initiative and the performance measures.
2.3. Different business strategies and critical processes
Firms can achieve a competitive advantage by implementing
one of two fundamental types of business strategies and business
models to generate revenue: product differentiation and opera-
tional excellence
3
(Bentley et al., 2013; Grant, 2010). Grant (2010,
222) notes that:
“The two sources of competitive advantage de?ne two funda-
mentally different approaches to business strategy. A ?rmthat is
competing on low cost is distinguishable from a ?rm that
competes through differentiation in terms of market posi-
tioning, resources and capabilities, and organizational
characteristics.”
The two strategies imply that different business processes will
be critical processes. A product differentiation strategy requires a
highly effective New Product Development process to produce
innovative, distinctive new products (Grant, 2010), causing this
process to be a critical process. An example is the importance of
NewProduct Development at Apple. Given the importance of being
“?rst to market”, product differentiation ?rms need to con?gure
complex Production setups rapidly and then coordinate sophisti-
cated Production activities. Therefore, Production is a critical pro-
cess. Customer Service is also a critical process because a product
differentiation ?rm's customers expect highly informed and
responsive service given the expectations created by the brand and
relatively high product prices paid (Grant, 2010; also see Schultz
et al., 2010). Therefore, the critical processes for a ?rm pursing a
business model based on a product differentiation strategy are New
Product Development, Production, and Customer Service. The
number and success of new products introduced during the period,
combined with sales of existing products, would imply a level of
demand and a revenue number for the period (Peecher et al., 2007).
Within the same industry, a ?rm that implements an opera-
tional excellence strategy, which focuses on cost minimization,
provides a basis of comparison with the business risks of a product
differentiation ?rm. Operational excellence imposes different de-
mands on a ?rm's value chain and, therefore, different processes
become critical processes. The objective of an operational excel-
lence strategy is to use contemporary but standardized product
designs to minimize product costs and compete based on a rela-
tively low product price (Grant, 2010). Procurement (inbound lo-
gistics) is a critical process given the importance of optimizing
value chain logistics and minimizing unit costs. The Production
process is also a critical process but for different reasons compared
with a product differentiation strategy, i.e., the ?rm designs prod-
ucts for ease of ef?cient Production versus products that are com-
plex to manufacture (Grant, 2010). In order to offer low prices, the
?rm must achieve highly ef?cient Production of standardized
products produced at minimum levels of unit cost (Grant, 2010).
The third critical process is Distribution and Sales (outbound lo-
gistics). Ef?cient distribution of products to customers is essential
to minimize logistical costs. Therefore, Procurement, Production,
and Distribution and Sales are the three critical processes of an
operational excellence strategy.
2.4. Process business risk and the risk of material misstatement of
revenue (H1)
Business risk auditing emphasizes the relevance of the business
risk of processes that are critical for ?nancial statement results and
their possible misstatement (Peecher et al., 2007)
4
(see Fig. 1).
Process-speci?c business risk re?ects the consequences of levels of
process performance (IFAC, 2012: ISA 315R). Two reasons why a
client's critical processes could cause revenue to be materially
misstated are: (1) failure to achieve one or more strategic objectives
results in less customer demand, which stimulates management to
manage earnings and (2) errors are introduced by changes in the
design of a critical process that may also result in earnings
Link#1 Link#2 and Hypothesis 1
Link #3 and Hypothesis 2
(mediation)
Process-level business
risk of a process that is
a critical process
Levels of performance
of a process that is a
critical process
Risk of material
misstatement of
revenue
Fig. 1. Tested structural relationships for judgments (Hypotheses 1, 2, and 3ab).
3
Miles and Snow (1978, 2003) describe different business strategies that may
exist within an industry (Bentley et al., 2013). A product differentiation strategy
would be a prospector strategy while an operational excellence strategy would be a
defender strategy.
4
The client's operations imply the RMM, i.e., RMM is an operating concept:
“According to the current authoritative guidance in the US, RMM exists indepen-
dently of the audit, and is assessed by the auditor through the exercise of profes-
sional judgment” (Peecher et al., 2007, 468).
W.F. Wright / Accounting, Organizations and Society 48 (2016) 43e55 46
management activities.
When the operation of a critical process yields less customer
demand than management expected, management may engage in
real and/or accrual-based earnings management activities (Schrand
& Zechman, 2012). More generally, in the context of ISA 315 (IFAC,
2012), Schultz et al. (2010, 239e240) indicate, “Poor performance
may lead to increased business risk which in turn may “indicate the
potential risk of management bias in the preparation of the ?nan-
cial statements (ISA, 35)”.”
5
Concerning revenue recognition,
management may report sales that should be recognized in the
following period using channel stuf?ng of products, offering of
unwarranted discounts and rebates, and granting of excessively
liberal product return provisions. Management may also bias
revenue-oriented accruals such as sales returns and allowances.
The collectability of receivables for sales made at the end of a period
on overly lenient credit terms may be overstated. Instances of fraud
are feasible including recognition of sales beyond a cutoff date.
6
The second cause of possible misstatement is the presence of
errors in the recording of sales caused by an effort to improve the
operation of a critical process. Competitive pressures provide
management with ongoing incentives to improve the effectiveness
of critical processes. However, process design changes can involve
risky, sometimes disruptive periods of adjustment and learning
(Grant, 2010). Process changes may introduce errors because they
can involve re-con?guration of work activities, requiring re-
training of personnel and integration of new employees with
different skill sets (Peecher et al., 2007). An example is Hershey's
failed initial attempt to change its business processes and infor-
mation system, and the errors that resulted for revenue recognition
(Perepu, 2008; Stratman, 2007). More generally, process changes
may introduce order ful?llment errors into the evidence that is the
basis for recording of sales. Disruptive process changes that reduce
product quality may result in higher than expected levels of sales
returns and allowances. Awareness of such problems may motivate
management to engage in earnings management.
Processes vary in their relative exposure to each of these two
causes of business risk depending on where a process is in the
client's value chain. For example, NewProduct Development would
be more subject to the risk of failing to meet customer objectives
while Distribution and Sales would be more subject to errors in the
recording of sales.
Hypothesis 1 (stated in alternative form) predicts that the par-
ticipants will assess a positive association for levels of business risk
and the RMM of revenue for the critical processes e and a lack of
association for the non-critical processes.
H1. There will be a signi?cant positive association between
judgments of process-speci?c business risk and the RMM of reve-
nue for critical processes, and no association for business processes
that are not critical processes.
2.5. Process performance and the risk of material misstatement:
business risk as a mediating variable (H2)
For processes that are critical given management's strategy, one
would expect lower levels of process performance to be associated
with higher levels of the RMMof revenue (Knechel et al., 2010) (see
Fig. 1). This association is partially due to the business risk of the
process, i.e., the consequences of a critical process not contributing
to the achievement of one or more customer-oriented strategic
objectives (see Fig. 1). This is especially true for a ?rmthat pursues a
product differentiation strategy given the critical importance of
achieving the constant level of signi?cant product innovation ex-
pected by the ?rm's customers.
Managements that pursue a product differentiation strategy
(e.g., Apple) accept the need for continuous success in newproduct
development and manufacturing of new products, as well as pro-
vision of excellent customer service. These demanding and risky
product innovation objectives (Bentley et al., 2013; Soliman, 2008)
generate higher process-speci?c business risks; hence, these busi-
ness risks can meditate the relationship between levels of process
performance and the RMM of revenue (Schultz et al., 2010). The
second hypothesis predicts that the participants will recognize that
the business risk of the critical processes mediates the relationship
between levels of process performance and the RMMof revenue for
a product differentiation strategy. The critical processes are New
Product Development, Production and Customer Service.
H2. For critical processes given a product differentiation strategy,
process-speci?c business risk will mediate the relationship be-
tween judgments of levels of business process performance and the
RMM of revenue.
2.6. Product generation business risk and the risk of material
misstatement of revenue (H3ab)
The ?nal hypotheses address whether the participants will
distinguish the differences in the RMM of revenue for the two
business strategies contingent on the operating performance and
business risk of a pro?le of the three product generation processes
(see Fig. 1): New Product Development, Procurement and Produc-
tion. These three processes are fundamental for a manufacturing
?rm, especially in an industry where product innovation is
important (Terziovski, 2010).
The two strategies imply different competitive conditions for
generation of products and different pressures for earnings man-
agement. Given the customer demands for constant product
innovation and effective, timely Production, a product differentia-
tion business strategy results in a higher level of product innovation
business risk (Bentley et al., 2013; Soliman, 2008). Alternatively, a
?rm pursuing an operational excellence strategy generates stan-
dardized products without the same level of product innovation
business risk.
Moreover, when the performance pro?le of the three product
generation processes indicates relatively low performance, the
risks of a product differentiation strategy imply more business risk
and a higher RMM of revenue. Given that customers have high
expectations for new products, achievement of the client's
demand-oriented objectives will be less likely. Any decrease in
customer demand implies more attention to risky business process
change and stronger incentives for earnings management. There-
fore, the RMM of revenue given a product differentiation strategy
would be highest when product generation performance is rela-
tively low. In contrast, when the performance of the three product
generation processes is relatively high and business risk is rela-
tively low, judgments of the RMM of revenue are predicted to be
5
“Business risk is broader than the risk of material misstatement of the ?nancial
statements, although it includes the latter. Business risk may arise from change or
complexity. A failure to recognize the need for change may also give rise to business
risk. Business risk may arise, for example, from: (1) The development of new
products or services that may fail; (2) A market which, even if successfully devel-
oped, is inadequate to support a product or service; or (3) Flaws in a product or
service that may result in liabilities and reputational risk.” (numbers replaced
bullets) (IFAC, 2012: ISA 315R, A37).
6
Overstating revenue is a very common type of accounting fraud. Hogan, Rezaee,
Riley, and Velury (2008) report that between 38% and 50% of frauds involved
overstating revenues.
W.F. Wright / Accounting, Organizations and Society 48 (2016) 43e55 47
lower and similar for both strategies. Adding the expected lower
RMM of revenue for the operational excellence strategy when
product generation performance is relatively low, the expectation
for the four cell means is an ordinal interaction (see Fig. 4). The
following hypotheses are implied:
H3a. Given relatively low product generation performance and
higher business risk, the participants will assign the highest ex-
pected RMM of revenue to a ?rm that implements a product dif-
ferentiation versus an operational excellence business strategy.
H3b. Given relatively high product generation performance and
lower business risk, the participants will assign the same expected
RMM of revenue to both strategies.
3. Method
3.1. Participants
Acting as proxies for staff auditors, the participants are 133 ac-
counting Masters students who were a few weeks from their
graduation. Approximately ninety-?ve percent of the students had
completed one summer internship and sixty-?ve percent had
completed two, usually with a major accounting ?rm. Trompeter
and Wright (2010) note that less experienced audit staff are
increasingly doing assessment of business risks.
The graduate accounting students are suitable participants for
testing of the hypotheses. They had essentially completed a grad-
uate accounting curriculum that had more of a general business
versus a ?nancial accounting orientation. The graduate students
had studied the ideas of business risk auditing (especially Bell et al.,
1997, 2002, 2005), business strategies and the operation of business
processes. Therefore, their knowledge was suf?cient for the judg-
ment tasks, providing for an internally valid test of the theory
(Peecher & Solomon, 2001). The students had not seen any of the
materials (or any similar to them) during class sessions.
3.2. Design
Underlying the judgments is a 2 (strategy orientation) x 2
(pro?les of product generation performance) randomized between
subjects design. The ?rst manipulated factor is the client's business
strategy (see Fig. 2). While both companies are in the same in-
dustry, the business models of the two companies incorporate
either a product differentiation (Cornerstone) or an operational
excellence (Belmont) business strategy (Kaplan & Norton, 2000).
The strategy determines which business processes are the critical
and non-critical processes.
The second manipulated factor is two pro?les of facts that
indicate either relatively low or high performance for the three
product generation processes (Terziovski, 2010): New Product
Development, Procurement and Production (see below). The pro-
?les were generated by starting with what would be the relatively
“low” performance pro?le and changing the levels of seven key
process performance metrics for the three processes to more
favorable levels, e.g., “decreased” to “increased”, 2.5% to 3.0%,
“fewer” to “more”, etc. (see the appendix). Use of the two pro?les
enables testing of the predicted difference in the relative RMM of
revenue for the two strategies given two levels of product gener-
ation performance (H3a and H3b). The participants completed a
manipulation check to determine whether the two pro?les depic-
ted distinct levels of product generation performance. The facts for
both the Distribution and Sales process and the Customer Service
process were not manipulated.
3.3. Context and judgments
The context is assessment of business risk and RMM of revenue
prior to tests of controls and during application of preliminary
analytical procedures. This research employs a simpli?ed yet
comprehensive representation of the value chain of a
manufacturing ?rm (American Productivity and Quality Center,
2014; Supply Chain Council, 2012). The participants used case
materials for one of two companies in the same industry:
manufacturing and marketing of graphics boards (see Figs. 2 and 3).
Having the two companies in the same industry controls for overall
industry economic conditions and involves the same operational
business processes. The graphics board industry is extremely
competitive with the need for continuous development of new
products.
7
Each client's business strategy and strategic objectives,
and the actions of its competitors, determine the inherent risks of
misstated ?nancial statement amounts. Both companies reported
the same increasing sales over the last four years. In addition, both
companies indicated commitment to increasing their gross and
operating margin percentages relative to the percentages of a
benchmarked highly successful ?rm in the industry (Knechel et al.,
2010). The two company descriptions are based on actual company
information. Audited ?nancial information was provided including
four years of revenue results and detailed ?nancial data for the
current year. No problems are indicated for the prevailing ?nancial
controls.
The participants provided process performance and process-
speci?c business risk judgments for the ?ve primary business
processes.
8
These ?ve processes include: (1) New Product Devel-
opment, from product ideas to viable products ready for
manufacturing; (2) Procurement, i.e., inbound logistics, which is
the process of obtaining inputs for Production of products in the
quantities needed, when they are needed, at a favorable price; (3)
Production, effective and ef?cient generation of products; (4) Dis-
tribution and Sales, i.e., outbound logistics and sales activities; and
(5) Customer Service, customer support (see Fig. 3). In addition, the
erentiation Orientation)
leader for sophisticated graphics cards for high-performance desktop and notebook
computers. Success in this market requires consistent introduction of innovative new
gaming enthusiasts, scientists and users of financial workstations who pay top dollar
rnal culture of
creativity and innovation to succeed. Venture capitalists funded most of the startup
costs associated with Cornerstone in 2001. Cornerstone had its IPO at the start of
2004.
(Operational Excellence Orientation)
market leader for standard, high quality graphics cards for desktop and notebook
computers. Success in this market requires excellent value chain efficiencies,
including effective relationships with suppliers, consistent reductions in input and
production costs, and efficient production of high quality graphics cards. Venture
capitalists funded most of the start up costs associated with Belmont in 2001.
Belmont had its IPO at the start of 2004.
Fig. 2. The two business strategies and strategic objectives.
7
For example: “The market for our products is extremely competitive, and we
expect competition to intensify as current competitors expand their product of-
ferings, industry standards continue to evolve and others realize the market po-
tential of mobile and consumer products and service.” (NVIDIA, 2012 10 K, item1a.).
8
Primary value chain processes (e.g., Production) transform ideas and inputs into
products or services while support processes (e.g., human resource management)
sustain the operation of the primary processes (Grant, 2010).
W.F. Wright / Accounting, Organizations and Society 48 (2016) 43e55 48
participants completed three account-level RMM assessments (see
below).
When the participants provided both their assessment of the
performance and the business risk of each of the ?ve processes they
were told (emphasis in the original):
“For the next set of questions, given the information available in
this case, please indicate your evaluation of the overall level of
business process performance and the level of business risk for
each of the ?ve fundamental processes of [their company].
De?ne business risk as “the degree of the uncertainty that a
business process will not be suf?ciently effective to implement
the client's strategy successfully to its customers.”
As an example, the question for the New Product Development
process is as follows: “What is your assessment of the overall
performance of [their company's] new product development pro-
cess?” Judgments were reported on a 1 to 9 scale with ?ve being
average industry performance as a benchmark. For the process-
speci?c business risk assessment, the participants were asked:
“What is your assessment of the business risk of [their company's]
new product development process?” Judgments were made on a
scale of 0e100% with one anchor being “Lowest risk” and the other
anchor being “Highest risk.” The participants completed the same
two questions for the other four processes.
Finally, the participants reported the level of RMM for the cli-
ent's revenue, as well as the RMMof the Cost of Goods Sold and the
Selling and Administrative Expense accounts. The last two RMM
judgments were included to avoid excessive concentration on the
RMM of revenue judgment; they are not involved in the analysis.
3.4. Procedure
After reading an introduction to the auditor's risk assessment
context, the participants read one of the two descriptions of a cli-
ent's strategic orientation (see Fig. 2). Next, the participants read
about prevailing conditions and competition in the (same) industry.
Then the participants considered detailed information about the
operation of each of the client's ?ve primary business processes
(see Fig. 3). Finally, the client's current unaudited ?nancial results
were presented with audited results from prior years, bench-
marked against the results of a major competitor (Knechel et al.,
2010).
The participants then began to reach their conclusions. First,
New Product
Development
Procurement (of
inputs)
Production Distribution and
Sales
Customer
Service
Cornerstone (product differentiation orientation) and the relatively low product generation performance
profile of the New Product Development, Procurement and Production processes (one of 2x2 conditions)
New Product Development
The 100 members of Cornerstone’s new product development (New Product Development) team
focus on designing innovative graphics cards that enhance the performance of computers.
Cornerstone emphasizes hiring creative and highly motivated employees in New Product
Development. Cornerstone R&D personnel work directly with suppliers to specify the
functionality of the parts that they need to create innovative new products. The people in R&D
receive significant bonuses for inventions that get patents. However, a serious issue is that fewer
new products were introduced in 2005 compared with the number of new products introduced in
2004 and in 2003.
Cornerstone’s R&D expenditures have been decreased in 2005 as a percent of revenue from 10.5%
in 2004 to 7.0% in 2005. In contrast, ATI spent 14.7% of sales in 2005 on R&D.
Procurement
Several specialized suppliers are used to procure the parts for Cornerstone’s graphics cards.
Many suppliers have set-up operations in the area surrounding Cornerstone’s facility in Northern
California. Cornerstone is not very demanding regarding the prices and terms it requires from
suppliers.
Production
Cornerstone has an effective automated system that orders parts when the inventory of a part is
below a certain threshold. Production of graphics cards is managed by a state-of-the-art IT
system. The IT system operates the assembly line which consists of many precision
manufacturing machines. Production efficiency reached 99.1% in 2005 which is significantly
below the industry average. The production scheduling software does not always make the best
decision and costly adjustments must be made frequently.
Distribution and Sales
Cornerstone sells to computer manufacturers and major retailers. Orders received from
customers are processed rapidly with virtually no errors. The distribution of finished products
also works effectively and efficiently. Timely delivery of products to customers occurs after
management outsourced product logistics and shipping to United Parcel Service (UPS) in early
2004.
Customer Service and Support
Cornerstone emphasizes good customer service and technical support. Questions received from
customers are handled by an experienced staff. The customer support staff typically receives high
levels of satisfaction from customers. Most cards are ensured for two years of use.
Fig. 3. Primary operating business processes in the value chain.
W.F. Wright / Accounting, Organizations and Society 48 (2016) 43e55 49
they decided which business strategy applied (Table 1, Panel A).
Second, they assessed the performance and the associated business
risk of each of the ?ve processes. Finally, the participants reported
the three RMM assessments.
4. Results
4.1. Business strategy and pro?le performance manipulation checks
Each of the 133 participants chose which strategy type applied
given the case information. (Sixty-four participants correctly chose
the product differentiation strategy (the Cornerstone version of the
case); 51 correctly chose the operational excellence strategy (the
Belmont case) (see Table 1, Panel A). Therefore, 115 participants
chose the intended strategy orientation and satis?ed the manipu-
lation check (Fig. 2).
9
All of the results in the paper utilize the 115
participants.
There are two product generation performance pro?les. The two
pro?les differentiated signi?cantly between relatively lowand high
pro?les of product generation performance. A multivariate test of
the difference between the two pro?les of three process perfor-
mance means is signi?cant, F(3,111) ¼ 98.94, p < .001. Also signif-
icant is the expected difference in process performance for each of
the three product generation processes, p < .0001 (Table 1, Panel B).
4.2. Judgments of process-speci?c business risk for the critical
processes
The expectation is higher process-speci?c business risk assess-
ment when a process is a critical process (Peecher et al., 2007). For
the product differentiation strategy, the prediction is higher busi-
ness risk for the New Product Development and Customer Service
processes relative to the same processes given the operational
excellence strategy. Alternatively, for the operational excellence
strategy, the prediction is higher business risk for the Procurement
and the Distribution and Sales processes. For both strategies, the
Production process is a critical process, albeit for different reasons;
a difference in levels of business risk is not expected.
The mean levels of business risk for the critical processes for
each of the two strategies are reported in Table 2. For the product
differentiation strategy, the mean business risk for the NewProduct
Development process is signi?cantly higher, 64.13 vs. 55.55,
t ¼ 2.183, p ¼ .015, as is the business risk of the Customer Service
process, 31.84 versus 26.47, t ¼ 1.673, p ¼ .048. (The theory implies
one-tailed tests unless otherwise noted.) For the Production pro-
cess, which is a critical process for both strategies, as was predicted,
the levels of business risk are not signi?cantly different, i.e., 63.02
for the product differentiation strategy versus 58.76 for the oper-
ational excellence strategy (t ¼ 1.104, p ¼ .272, two-tailed). How-
ever, for the operational excellence strategy, inconsistent with
predictions, the mean Procurement business risk is not signi?cantly
higher, 46.27 versus 46.33, t ¼ .013, p ¼ .495, nor is the mean
business risk for the Distribution and Sales process, 34.56 versus
31.07, t ¼ .954, p ¼ .171.
Table 1
Business strategy and process pro?le performance judgments.
Panel A: Business strategy applied by the participants and the strategic orientation in the case.
Business strategy applied by participants Total
Product differentiation Operational excellence
Strategic orientation Cornerstone (Product differentiation) 64 5 69
Belmont (Operational excellence) 13 51 64
Total 77 56 133
All of the results in the paper are based on the 115 (64 and 51) participants who satis?ed the manipulation check.
Panel B: Mean (standard deviation) of judgments of the level of performance of the ?ve primary business processes for the two performance pro?les
New product development Procurement Production Distribution and sales Customer service
Lower pro?le performance 3.98 (1.57) 4.70 (1.42) 3.19 (1.43) 7.07 (.95) 7.10 (.91)
Higher pro?le performance 6.69 (.84) 6.76 (1.41) 6.99 (1.11) 7.20 (1.27) 7.24 (1.04)
Signi?cance of difference in means t ¼ 11.76, p < .0001 t ¼ 7.79, p < .0001 t ¼ 16.02, p < .0001 t < 1, n.s. t < 1, n.s.
The response scale for measurement of process performance is a 1 (“Lowest”) to 9 (“Highest”) scale with a midpoint of 5 (“Industry Average”). The two pro?les of facts indicate
relatively low or high levels of operating performance for the manipulated product generation processes of New Product Development, Procurement and Production, pro-
cesses that are fundamental for a high-technology manufacturing ?rm (Terziovski, 2010). The t-tests are one-tailed tests not assuming equal variances.
Table 2
Mean (standard deviation) of process-speci?c business risk judgments for the ?ve primary business processes for each of the two business strategies.
Process (down), strategy (across) Product
differentiation
Operational
excellence
t-Statistic, signi?cance (all one-tailed except for Production) Consistent with
prediction?
New product development 64.13
(21.42)
55.55
(20.29)
2.183, p ¼ .015 Yes
Customer service 31.84
(19.98)
26.47
(12.57)
1.673, p ¼ .048 Yes
Production 63.02
(21.04)
58.76
(19.85)
1.104, p ¼ .272 Yes
Procurement 46.33
(22.77)
46.27
(21.48)
.013, p ¼ .495 No
Distribution & sales 31.07
(18.48)
34.56
(20.73)
.954, p ¼ .171 No
The means indicated in bold italics and underlined are predicted to be signi?cantly higher. The de?nition of business risk is “The degree of the uncertainty that a business
process will not be suf?ciently effective to implement the client's strategy successfully to its customers.” There are no meaningful differences between the t-statistics with and
without the assumption of equal cell variances. The number of participants is 115.
9
Use of all 133 participants yields very similar results.
W.F. Wright / Accounting, Organizations and Society 48 (2016) 43e55 50
4.3. Process-speci?c business risk and the risk of material
misstatement of revenue (H1)
Hypothesis 1 tests for the predicted association of process-
speci?c business risk for critical processes with the RMM of reve-
nue (see Fig. 1). For the product differentiation business strategy
(Table 3, Panel A), H1 predicts signi?cant coef?cients for the busi-
ness risks of the New Product Development, Production and
Customer Service processes. The standardized regression co-
ef?cients are signi?cant for both the New Product Development
(.371, p ¼ .002) and the Production processes (.240, p ¼ .026), but
not for the Customer Service process (.153, p ¼ .127). As was pre-
dicted, for the processes that are not critical processes, i.e., Pro-
curement and the Distribution and Sales processes, the coef?cients
are not signi?cant, t < 1. The model is signi?cant, F(5, 58) ¼ 8.349,
p < .001, with an adjusted R
2
of .368.
For the operational excellence business strategy (Table 3, Panel
B), H1 predicts signi?cant regression coef?cients for the business
risk of the Procurement, Production and Distribution and Sales
processes. Consistent with H1, the standardized coef?cient for the
Distribution and Sales process is signi?cant (.413, p ¼ .001), how-
ever, the coef?cient for the Procurement process is not (.148,
p ¼ .156). The business risk of the Production process is not sig-
ni?cant (t < 1). As was predicted, the coef?cient for the business
risk of the Customer Service process is not signi?cant (p ¼ .210).
Contrary to expectation, the coef?cient for the business risk of the
New Product Development process is signi?cant (.439, p ¼ .002).
Overall, the model is signi?cant, F(5, 45) ¼ 6.549, p < .001, with an
adjusted R
2
of .357.
4.4. Business risk mediation of process performance and the RMM
of revenue (H2)
H2 predicts that, for the product differentiation strategy, judg-
ments of process-speci?c business risk for critical processes will
mediate the association between judgments of process perfor-
mance and the RMM of revenue (see Fig. 1).
10
I use the Baron and
Kenny (1986) structural analysis to test for mediation (Iacobucci,
2008; Iacobucci, Saldanha, & Deng, 2007). The mediation analysis
includes three linkages (Fig. 1). Link #1 tests for whether levels of
process performance are associated with levels of process-speci?c
business risk for each of the three critical processes, i.e., will
judgments of lower process performance be correlated with higher
levels of process business risk? All three processes have the ex-
pected negative and signi?cant relationship with standardized
slope coef?cients of À.424, p < .001 (New Product
Development), À.610, p < .001 (Production) and À.334, p ¼ .004
(Customer Service). The results indicate support for link #1 of the
mediation model for each critical process.
Link #2 tests for linear association between levels of process-
speci?c business risk and the RMM of revenue for each of the
three critical processes (see the results reported above for the
testing of H1 and Panel A of Table 3). The standardized slope co-
ef?cient is signi?cant for both the New Product Development (.371,
p ¼ .002) and the Production (.240, p ¼ .026) processes; the coef-
?cient for the Customer Service process is not signi?cant (.153,
p ¼ .127). These results provide support for link #2 for the New
Product Development and Production processes.
Link #3 is the association between judgments of the perfor-
mance of a critical process and the RMM of revenue (see Fig. 1). A
signi?cant correlation for a critical process would be partially due
to the business risk implications of the process for the client's ob-
jectives and the RMM of revenue. Therefore, I test for whether the
process-speci?c business risk judgments will mediate the associ-
ation between judgments of process performance and the RMM of
revenue (H2). The standardized regression coef?cient of process
performance for the Production process (À.358, p ¼ .035) is sig-
ni?cant; the coef?cient for the New Product Development process
(À.200, p ¼ .130) and for the Customer Service process (.111,
p ¼.225) are not signi?cant (Table 4, Panel A). The regression model
is signi?cant, F(5,58) ¼ 2.945, p ¼ .019, with an adjusted R
2
of .134.
Therefore, the test of H2 will be for business risk mediation of the
association between the performance of the Production process
and the RMM of revenue.
The test for mediation involves determining whether there is a
signi?cant decrease in the slope coef?cient for the process perfor-
mance judgments when the business risk assessments are added to
the regression. Panel B of Table 4 reports the results of regressing
Table 3
Impact of the ?ve process-speci?c business risks on the risk of material misstatement of revenue.
Panel A: Model for the product differentiation strategy
Process-speci?c business risks Coef?cient Std. Error Standardized coef?cient t-Statistic Signi?cance of slopes is one-tailed Consistent with prediction?
(Constant) 13.835 7.325 1.889 .064
New product development .337 .113 .371 2.971 .002 Yes
Procurement .037 .094 .043 .396 .347 Yes
Production .222 .112 .240 1.989 .026 Yes
Distribution & Sales .079 .136 .075 .582 .282 Yes
Customer service .149 .129 .153 1.153 .127 No
Model: F(5, 58) ¼ 8.349, p < .001; adjusted R2 ¼ .368, N ¼ 64.
Panel B: Model for the operational excellence strategy
Process-speci?c business risks Coef?cient Std. Error Standardized coef?cient t-Statistic Signi?cance of slopes is one-tailed Consistent with prediction?
(Constant) 13.675 8.030 1.703 .095
New product development .420 .138 .439 3.048 .002 No
a
Procurement .133 .130 .148 1.023 .156 No
Production .008 .141 .008 .055 .478 No
Distribution & Sales .387 .125 .413 3.091 .001 Yes
Customer Service À.184 .226 À.119 À.816 .210 Yes
Model: F(5, 45) ¼ 6.549, p < .001; adjusted R
2
¼ .357, N ¼ 51.
Business risk is de?ned as “The degree of the uncertainty that a business process will not be suf?ciently effective to implement the client's strategy successfully to its cus-
tomers.” The critical processes are indicated in bold italics.
a
The coef?cient for New Product Development was not expected to be signi?cant. Please see the text for further explanation.
10
As was expected for the operational excellence strategy, judgments of business
risk did not mediate the relationship of process performance and the RMM of
revenue for the critical processes.
W.F. Wright / Accounting, Organizations and Society 48 (2016) 43e55 51
only the Production process performance judgments on the RMM
of revenue judgments; the association remains signi?cant (À.399,
p ¼ .001) without any effects of the other four processes contrib-
uting to the signi?cance of the Production process judgments
(compare the results in Panels A and B).
Consistent with H2, adding the business risk assessments to the
regression model reported in Panel B causes the coef?cient for the
performance of the Production process to decrease such that it is
insigni?cant.
11
The Production business risk coef?cient is signi?-
cant (.379, p ¼ .005) and the process performance coef?cient for
the Production process is no longer signi?cant (p ¼ .118) (Panel C).
The magnitude of the mediation is indicated by a reduction in the
absolute value of the regression coef?cient for the performance of
the Production process, i.e., j3.235e1.360j ¼ 1.875 (see Panel B and
C). The Sobel ratio test is signi?cant, À2.971, p ¼ .003. The adjusted
R
2
for the model that includes only the performance of the Pro-
duction process is .146 (Panel B) e the adjusted R
2
increases to .225
with the inclusion of the process-speci?c business risk judgments
(Panel C). In summary, the judgments of the business risk of the
Production process mediate the association between the judg-
ments of process performance and the RMM of revenue.
4.5. Product generation business risk for the product differentiation
strategy (H3a and H3b)
Hypothesis 3a predicts that the participants will assess a higher
RMM of revenue for the ?rm that implemented the product dif-
ferentiation strategy when the pro?le of the three product gener-
ation processes indicates relatively low performance and higher
business risk. Hypothesis 3b predicts that when product generation
performance is relatively high and the business risk is relatively
low, the RMM of revenue judgments will be lower and will not be
signi?cantly different for the two strategies. Also predicted is a
lower RMM of revenue for the operational excellence strategy
when product generation performance is relatively low. The four
cell means are reported in Table 5, Panel A and are displayed as
Fig. 4.
Considered simultaneously, the hypotheses predict the highest
RMM for the product differentiation strategy when product gen-
eration performance is relatively low and lower RMM for both
strategies when product generation performance is relatively high.
Contrast coding is most appropriate in terms of statistical power to
test the predicted ordinal interaction (Buckless & Ravenscroft,
1990; Keppel & Wickens, 2004). The results are reported in
Table 5, Panel B.
12
The contrast weights are þ1 for the product
differentiation strategy and lower pro?le performance versus À1/3
for each of the other three cell means. The contrast is signi?cant,
F(1,111) ¼ 10.960, p ¼ .001. The contrast is effective given that the
between-groups residual variance is not signi?cant.
Testing hypothesis H3a, when pro?le performance is relatively
low, the RMM is signi?cantly higher for the participants who
received the product differentiation strategy versus those who
received the operational excellence strategy, 64.11 > 54.63, t ¼1.98,
p ¼ .026 (Table 5, Panel B). The results are consistent with H3a.
Hypothesis 3b predicts lower and similar RMMjudgments when
product generation performance is relatively high. The mean
judgments of the RMM of revenue are 50.41 for the product dif-
ferentiation ?rm versus 49.93 for the operational excellence ?rm,
the difference not being signi?cant, t < 1, n.s. The results are
consistent with H3b.
Table 4
Business risk mediation of process performance for the RMM of revenue (product differentiation strategy). The business processes that are critical processes are indicated in
bold italics.
Panel A: Regression of process performance on the RMM of revenue
Performance of process Coef?cient Std. Error Stand. Coeff. t-Statistic Signi?cance of slopes is one-tailed
(Constant) 52.706 18.501 2.849 .006
New product development À2.088 1.837 À.200 À1.137 .130
Procurement 1.359 1.783 .118 .762 .224
Production À2.899 1.563 À.358 À1.855 .035
Distribution & Sales .989 2.672 .056 .370 .357
Customer service 2.224 2.932 .111 .758 .225
Model: F(5,58) ¼ 2.945, p ¼ .019; adjusted R2 ¼ .134, N ¼ 64.
Panel B: Regression of performance of Production process on the RMM of revenue
Performance of process Coef?cient Std. Error Stand. Coeff. t-statistic Signi?cance of slope is one-tailed
(Constant) 73.713 5.019 14.687 mean RMM
(OperExcellence, lower performance), 64.11 > 54.63, t ¼1.983, p ¼.026, one-
tailed*;
b.2 Hypothesis 3b: Mean RMM (ProdDiff, higher performance) ¼ mean RMM
(OperExcellence, higher performance), 50.41 and 49.93, t < 1;
*Mean RMM (ProdDiff, lower performance) > mean RMM (ProdDiff, higher per-
formance, 64.11 > 50.41, t ¼ 2.95, p ¼ .002, one-tailed.
RMM is the risk of material misstatement of revenue. Product differentiation
(ProdDiff) and operational excellence (OperExcellence)) are the two strategies. The
second factor is two pro?les of facts that indicate relatively low or high levels of
performance for the product generation processes of New Product Development,
Procurement and Production, processes that are fundamental for a high-technology
manufacturing ?rm (Terziovski, 2010).
The null hypothesis of equal cell variances is not rejected (Levene's test),
F(3,111) ¼ 1.870, p ¼ .139. Given the number of participants is unequal across the
four cells, I estimated the ANOVA results using both Type I and Type III sums of
squares without any meaningful differences.
Fig. 4. Risk of material misstatement of revenue, two business strategies and two
pro?les of product generation performance.
W.F. Wright / Accounting, Organizations and Society 48 (2016) 43e55 53
in the RMM of revenue for the two strategies when product gen-
eration performance was relatively high and business risk was
relatively low.
One limitation of this research is use of a simpli?ed represen-
tation of a client's business processes and value chain. The goal was
to capture the essential determinants and impacts of process-level
business risk using comprehensive yet manageable materials. The
participants provided judgments consistent with the theory (with a
few exceptions). However, generalization of the ?ndings to actual
audit situations requires ?eld research (e.g., Curtis & Turley, 2007;
Eilifsen, Knechel, & Wallage, 2001).
A limitation of the design of the study is that it may understate
the ability of the participants to assess business risks generated by
different strategies. There are many similarities for the two ?rms.
Both ?rms are in the same highly competitive, technologically so-
phisticated industry (circuit board manufacturing). Both ?rms uti-
lize similar business processes, albeit with very different
implications given each of the two strategies. Moreover, the rela-
tively low and high performance pro?les indicate mixed implica-
tions for the success of each strategy; they are not intended to
convey obvious success, or a lack thereof, for a speci?c strategy.
(The measures are based on actual company data.) While use of
very different, tailored performance measures for each strategy
would make it easier to generate strategy effects in the experiment,
such an approach would cause the manipulations to be less subtle
(although it would make it easier for participants to determine the
?rm's strategy). Overall, the design results in conservative tests of
the hypotheses.
As is noted above, an unexpected result for the ?rm that
implemented an operational excellence strategy is the signi?cant
association between the judgments of the business risk of the New
Product Development process and the RMM of revenue. Even
though the ?rm offers more standardized products than it would if
it had implemented a product differentiation strategy, apparently
the very competitive, high-technology orientation of the graphics
board industry resulted in the participants assigning a signi?cant
association to New Product Development business risk and the
RMM of revenue.
The importance of understanding the operation of a client's
business and its competitive environment to achieve an effective
audit is well-known (PCAOB, 2010). More speci?cally, the PCAOB
(2010, 2), requires that an auditor understand “The company's ob-
jectives and strategies and those related business risks that might
reasonably be expected to result in risks of material misstatement
(emphasis in the original)”. Valid understanding also is necessary to
both interpret results from analytical procedures and to engage in
effective professional skepticism for management's assertions
(Nelson, 2009). For essentially newstaff auditors, the results reveal
a previously unreported level of understanding of process-oriented
business risks and their association with the RMM of revenue.
Appendix A. Supplementary data
Supplementary data related to this article can be found at http://
dx.doi.org/10.1016/j.aos.2015.11.005.
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