THE ROLE OF INDIVIDUAL PROFESSIONAL SKEPTICISM
IN FRAUD RISK BRAINSTORMING
Michelle McAllister
Florida State University
Email: [email protected]
Allen Blay
Associate Professor
Florida State University
Email: [email protected]
Kathryn Kadous
McIntyre Term Chair and Professor of Accounting
Goizueta Business School
Emory University
Email: [email protected]
Preliminary Draft. Do not cite or circulate without author permission.
Special thanks to Kenny Reynolds, Jeremy Douthit, and Bud Fennema for their helpful comments.
THE ROLE OF INDIVIDUAL PROFESSIONAL SKEPTICISM
IN FRAUD RISK BRAINSTORMING
Abstract
PCAOB AS 2110 Identifying and Assessing Risks of Material Misstatement provides auditors
with guidance on how to plan the audit in order to obtain reasonable assurance about whether the
financial statements are free of material misstatement due to fraud. Two key elements addressed in
PCAOB AS 2110 are the importance of exercising professional skepticism and the requirement of a fraud
brainstorming session. Despite this requirement, little is known about how individual professional
skepticism might affect the effectiveness and efficiency of responses to fraud risk indicators in group
settings. The findings of this study demonstrate that individual differences in trait professional skepticism
among group members in fraud risk brainstorming settings can significantly impact the quality of these
sessions. Including members with high levels of trait professional skepticism in the group significantly
increases the group’s perceived risk of fraud in both higher and lower risk settings. However, inclusion
of these members does not appear to significantly improve the group’s ability to identify relevant
underlying fraud hypotheses. This research contributes to the fraud brainstorming and professional
skepticism streams of research by demonstrating that the mix of individual differences in professional
skepticism among group members can significantly impact the outcomes of fraud risk brainstorming.
1
INTRODUCTION
The ability to detect fraudulent financial reporting in client financial statements continues to be of
utmost concern to auditors and regulators. PCAOB AS 2110, Identifying and Assessing Risks of Material
Misstatement, provides auditors with guidance on how to plan the audit in order to obtain reasonable
assurance about whether the financial statements are free of material misstatement due to fraud. Two key
elements addressed in PCAOB AS 2110 are the importance of exercising professional skepticism and the
requirement of a discussion among engagement personnel regarding the risks of material misstatement
due to fraud (AS 2110, paragraphs .49-.53). We examine how these elements operate jointly.
Specifically, this study addresses whether individual trait professional skepticism impacts the
effectiveness and efficiency of fraud risk brainstorming. It further examines whether one highly skeptical
member of a brainstorming group is sufficient, or whether more are needed for an impact.
The “discussion” referred to in PCAOB AS 2110 is intended to be operationalized as a group
brainstorming session among the audit team members with the purpose of identifying “how and where
they believe the entity's financial statements might be susceptible to material misstatement due to fraud,
how management could perpetrate and conceal fraudulent financial reporting, and how assets of the entity
could be misappropriated” (AS 2110). Beyond stating the purpose of the group brainstorming session,
and asserting that the discussion “should occur with an attitude that includes a questioning mind”,
consistent with the definition of professional skepticism, little guidance is provided as to how best to
conduct these sessions (AS 2110). Accordingly, accounting research has attempted to fill this gap by
exploring how audit team brainstorming affects fraud risk planning, along with methods for improving
such brainstorming sessions.
Previous accounting research finds evidence that brainstorming audit teams generate more high
quality fraud ideas than individual auditors, and are more effective at modifying standard audit
procedures in response to fraud risk indicators (Carpenter 2007; Brazel, Carpenter, and Jenkins 2010;
Hoffman and Zimbelman 2009). Previous research also indicates that computer-mediated, or electronic
2
brainstorming can significantly improve fraud brainstorming, yet in practice most brainstorming
conducted is face-to-face open brainstorming (Brazel et al. 2010; Lynch, Murthy, and Engle 2009).
Lastly, Brazel et al. (2010) propose three important elements to modeling brainstorming quality:
attendance and communication, brainstorming structure, and engagement team effort. Within the Brazel
et al. (2010) model, we propose auditors with higher assessed levels of trait professional skepticism who
attend brainstorming sessions may improve overall brainstorming quality by helping the group to more
accurately produce fraud risk assessments and identify fraud hypotheses.
PCAOB AS 2401 asserts that the characteristics of fraud make the auditor’s exercise of
professional skepticism throughout the audit of particular import and defines professional skepticism as
“an attitude that includes a questioning mind and a critical assessment of audit evidence” (AS 2401.13). 1, 2
In addition, PCAOB AS 2110 explicitly states the brainstorming discussion should occur with an attitude
of professional skepticism whereby team members “set aside any prior beliefs they might have that
management is honest and has integrity” (AS 2110.52). While extensive accounting research has
explored the link between individual professional skepticism and the evaluation of audit evidence and
assessments of fraud risk (Hurtt, Eining, and Plumlee 2008; Quadackers, Groot, and Wright 2009,
Quadackers, Groot, and Wright 2014), researchers have yet to consider how an individual’s level of
professional skepticism can impact the group as a whole. This is an extremely important question given
that audits are not completed by individuals, but rather by teams of auditors.
Recent auditing research identifies two primary components of individual professional
skepticism: skeptical judgment and skeptical action. Skeptical judgments relate to an auditor’s
recognition that a potential issue may exist, whereas skeptical actions refer to changes in auditor behavior
based on skeptical judgments (Hurtt, Brown-Liburd, Earley, Krishnamoorthy 2013). According to the
The specific requirements of the fraud brainstorming session are addressed in PCAOB AS 2110 Identifying and
Assessing Risks of Material Misstatement. However, PCAOB AS 2401 Consideration of Fraud in a Financial
Statement Audit speaks to all other aspects of the auditor’s responsibility as it relates to fraud.
2
PCAOB AS 2401 suggests fraud is difficult to detect because the perpetrators of fraud generally take steps to
conceal their actions and it is often difficult to determine management’s intentions.
1
3
model developed by Nelson (2009) and extended by Hurtt et al. (2013), one of the four broad inputs to an
auditor’s professional judgments is individual auditor characteristics. Individual auditor characteristics
include the auditor’s trait professional skepticism, experience and training, and motivation. Of particular
importance to this study is the concept of trait professional skepticism. Hurtt (2010) defines a trait as a
relatively stable, enduring aspect of an individual and she posits a link between an auditor’s trait
professional skepticism and skeptical judgments and actions. This link is validated in recent studies that
find that higher levels of inherent skepticism lead to differential evidence assessment (Hurtt, Eining, and
Plumlee 2008; Quadackers et al. 2009). While trait professional skepticism has been shown to affect
skeptical actions on an individual level, it is not clear how individual trait skepticism presents in a group
setting.
We conduct an experiment measuring individual trait professional skepticism using the Hurtt
(2010) professional skepticism scale. We place participants into groups based on their skepticism levels to
create groups that contain no highly skeptical individuals, one highly skeptical individual, and two highly
skeptical individuals. The participants first make an individual risk assessment and then participate in a
group brainstorming session in either a higher or lower fraud risk scenario. The setting is designed to
closely match the requirements of AS 2110.
The results of the study indicate that individual differences in inherent trait professional
skepticism can significantly impact the outcomes of fraud risk brainstorming groups. In both high and
low risk situations, we find evidence that groups that contain at least one member with high trait
professional skepticism evaluate the overall risk of fraud as higher than groups that do not contain
individuals with high levels of trait professional skepticism. Furthermore, we find no evidence of a
significant relationship between the number of members with high levels of trait professional skepticism
and the assessed risk of fraud when at least one member in the group possesses a high level of trait
professional skepticism. Because a significant difference in risk assessments persists across the higher
and lower risk settings even when high trait professional skeptics are in the group, we also conclude that
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the targeted inclusion of these individuals does not negatively impact the efficiency of the audit by
causing the group to over-estimate the risk of fraud in low-risk settings. However, we find no evidence
that including individuals with high trait professional skepticism in the group improves the group’s ability
to identify fraud hypotheses. Thus, while the synergistic properties of brainstorming make it possible for
those with high levels of professional skepticism to positively influence the skeptical awareness of the
group, it does not appear to help the group identify the underlying drivers of higher levels of risk.
Further, final post-brainstorming fraud risk assessments of participants with low levels of professional
skepticism were no higher when the individual was in a group with a highly skeptical individuals. In
sum, these findings indicate that including at least one highly skeptical auditor in brainstorming sessions
can increase audit effectiveness at least in terms of perceptions of fraud risk in brainstorming sessions, but
further research is necessary to determine the post-brainstorming effects on audit effectiveness.
The remainder of this paper proceeds as follows. First, we provide a summary of prior research
and develop our hypotheses. Second, we discuss our research design and experimental procedures.
Third, we provide a thorough analysis of our findings. Last, we provide several concluding thoughts,
limitations of the current study, and suggestions for future research.
PRIOR RESEARCH AND HYPOTHESIS DEVELOPMENT
Fraud Brainstorming
The purpose of fraud risk brainstorming as outlined in PCAOB AS 2110 is to help audit team
members consider how and where the entity’s financial statements might be susceptible to material
misstatement due to fraud and to reinforce the importance of an appropriate mindset of professional
skepticism. It requires auditors to complete a fraud brainstorming session as part of the planning stage of
an audit. Aside from outlining the purpose of the brainstorming session, the standard provides little
guidance concerning how to best conduct a proper brainstorming session. Perhaps as a consequence of
this lack of guidance, the PCAOB has noted several instances where auditors have failed to sufficiently
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comply with this auditing standard (PCAOB Release No. 2007-001). Indeed, recent accounting research
documents considerable variation in the reporting quality of fraud risk brainstorming across audit
engagements (Brazel et al. 2010). In response to this lack of guidance, accounting researchers, drawing
on previous psychology research, have attempted to determine the positive effects, if any, of fraud risk
brainstorming on audit quality and the best practices for conducting such brainstorming sessions.
The psychological research into brainstorming finds mixed results concerning the positive
outcomes of the practice. While some research in psychology posits that brainstorming can result in
performance improvements from cognitive stimulation and synergy among group members, many other
studies document significant drawbacks to the production quality of group brainstorming. 3 Production
blocking, social loafing, and evaluation apprehension are generally the primary reasons cited for poor
group brainstorming performance. Production blocking refers to a cognitive interference mechanism
whereby group participants forget their unique ideas while waiting for an opportunity to speak. Social
loafing implies that some participants may reduce their effort in the group even when they have the ability
to contribute important content to the group. Evaluation apprehension occurs when group members feel
intimidated about expressing an idea for fear that others might not like it (Dugosh, Leggett, Paulus,
Roland, and Yang 2000, Carpenter 2007; Hoffman and Zimbelman 2009). In addition to these commonly
cited drawbacks to group brainstorming, many social psychologists posit social comparison processing
may also play a role in the productivity of group brainstorming. Social comparison processing refers to
the idea that group members reduce their performance to match that of the least productive member of the
group (Dugosh et al. 2000; Camacho and Paulus 1995, Paulus and Dzindolet 1993). As an example,
Camacho and Paulus (1995) find that brainstorming groups in which the participants are mixed between
high socially anxious individuals and low socially anxious individuals perform significantly worse than
groups containing all low socially anxious individuals or high socially anxious individuals working alone.
3
See Carpenter (2007) for a review of the psychology literature concerning brainstorming.
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Despite the potential drawbacks cited in the psychology literature, accounting research documents
improvements in fraud risk planning due to group brainstorming. Accounting researchers generally
attribute these differences to the use of participants with specific subject knowledge. Most psychology
research conducted on brainstorming uses novices as subjects and employs tasks that do not require
specific knowledge or experience to complete. In contrast, individuals who participate in fraud risk
brainstorming experiments have experience and training in assessing the risk of fraud (Carpenter 2007;
Hoffman and Zimbelman 2009; Lynch, Murthy, and Engle 2009). Further, additional psychological
research indicates brainstorming quality is significantly improved when participants actively attend to the
ideas generated by other members of the group (Dugosh et al. 2000). It may be that participants in audit
research studies perform better in brainstorming groups relative to novice psychology subjects simply
because they are motivated to actively listen and engage in the discussion. This may be because audit
research participants have specific knowledge and insight to contribute during these sessions.
Carpenter (2007) first considered how fraud risk brainstorming might impact the evaluation of the
likelihood of fraud. In her experiment using hierarchical teams of three auditors, she found evidence that
while the quantity of fraud hypotheses created during fraud risk brainstorming is reduced, the hypotheses
generated are of a higher quality (Carpenter 2007). In a follow up study using audit managers, Hoffman
and Zimbelman (2009) found evidence that group fraud risk brainstorming can also lead to greater
adjustments to the nature, extent, and timing of standard audit procedures in high fraud risk situations.
Finally, using responses to auditor field surveys, Brazel et al. (2010) found additional evidence that highquality brainstorming, as measured using a 21 item self-report scale, improves the relation between fraud
risk factors and fraud risk assessments. The results of this study also imply that low-quality
brainstorming can lead to under-auditing (Brazel et al. 2010). Specifically, Brazel et al. (2010) find that
the extent to which testing procedures are increased or changed in high risk situations is significantly
lower for lower quality brainstorming sessions relative to high quality brainstorming sessions. The
implication of these results underscore the importance of identifying techniques that improve
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brainstorming quality. Developing a better understanding of how individual differences in trait
professional skepticism affect the effectiveness and efficiency of fraud risk brainstorming represents an
important opportunity to assist in this endeavor.
Brazel et al. (2010) propose a model of fraud brainstorming quality that indicates that the three
primary inputs of brainstorming quality are attendance and communication, brainstorming structure and
timing, and engagement team effort. Attendance and communication addresses who is present in the
brainstorming session and asserts that as more members of the audit team attend and engage in the
brainstorming session, brainstorming quality is improved due to greater diversity of thought.
Brainstorming structure and timing addresses the importance of holding the brainstorming session early in
the planning process in order to maximize improvements to individual auditor fraud judgments by
minimizing the possible negative effects of increased time pressure. Engagement team effort asserts that
greater team member preparation before, and participation during, the brainstorming session can
significantly improve brainstorming quality. Overall, this model suggests brainstorming quality impacts
the identification of fraud risk factors and hypotheses, which in turn causes the auditor to make changes to
the fraud risk assessment, which ultimately leads to changes in audit testing. Within this framework, the
present study seeks to understand how individual differences in trait professional skepticism of the
participants within a fraud brainstorming session can produce differential outcomes on brainstorming
quality. If individual differences in trait professional skepticism within a fraud brainstorming group
impact brainstorming quality, this represents a potential factor associated with the attendance and
communication dimension of fraud brainstorming quality articulated in Brazel et al. (2010).
Brazel et al. (2010) point specifically to the importance of holding the session early in the
planning process when considering the importance of brainstorming structure and timing in determining
overall brainstorming quality. In addition to timing, previous research also demonstrates that the method
used to conduct brainstorming can significantly impact brainstorming quality. For example, previous
research finds that brainstorming teams that are provided with brainstorming discussion guidance
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generate more fraud hypotheses than those with no such instruction (Trotman, Simnett, Khalifa 2009).
Furthermore, Lynch et al. (2009) find that overall brainstorming performance can be improved if
conducted using electronic brainstorming rather than a face-to-face configuration. However, Brazel et al.
(2010) document that in practice the overwhelming methodology used to conduct fraud brainstorming is a
face-to-face configuration despite the possible benefits of electronic brainstorming.
Professional Skepticism
Professional skepticism refers to “an attitude that includes a questioning mind and a critical
assessment of audit evidence” (AS 1015). Professional skepticism can be viewed as a lens through which
auditors evaluate evidence and risk throughout the audit process. This questioning attitude and behavior
is “essential to the performance of effective audits” and “is required in every aspect of every audit by
every auditor working on the audit” (Baumann 2012). The consistent application of professional
skepticism throughout the audit process has become a topic of increasing concern to regulators as
“PCAOB inspections have identified numerous audits with deficiencies where auditors did not
consistently and diligently apply professional skepticism” (Franzel 2013). Further, “this issue has been of
such prevalence that [the PCAOB] ha[s] identified the apparent failure to appropriately apply professional
skepticism as a systemic quality control issue in some firms” (Franzel 2013). In response to this concern,
recent accounting research has sought to model the determinants of professional skepticism in order to
develop strategies that positively impact the auditors’ application of skeptical judgments and decisions
throughout the audit process.
Recent research indicates that the determinants of professional skepticism should be split into two
distinct categories: skeptical judgments and skeptical actions (Nelson 2009; Hurtt 2010; Hurtt et al.
2013). Skeptical judgments refer to the auditor’s ability to recognize a potential problem, whereas
skeptical actions are those taken by auditors once a potential problem has been identified. Nelson (2009)
asserts that skeptical judgments and actions are affected by several factors including the auditor’s
incentives, traits, knowledge, audit experience and training, along with the engagement’s evidential input.
9
Hurtt et al. (2013) expand upon this model by categorizing these inputs into four categories: auditor
characteristics, evidential characteristics, client characteristics, and external environmental characteristics.
In this study we examine the interplay between the auditor characteristic of inherent trait professional
skepticism and the external environmental characteristic of group fraud brainstorming. Most prior
research concerning professional skepticism has focused on attempting to manipulate decision inputs in
order to observe how these inputs affect auditor skeptical actions. For example, research has shown that
auditors who prefer to frame a hypothesis in either a confirmatory or error-framed manner follow
different patterns of subsequent information searches (McMillan and White 1993). Previous research also
finds evidence that varying auditor incentives by manipulating partner emphasis on aspects of the
likelihood of fraud and the importance of professional skepticism produces differential skeptical actions
(Carpenter and Reimers 2013; Harding and Trotman 2011).
In contrast to research that attempts to evaluate auditor professional skepticism through decision
outcomes, recent research has attempted to measure trait professional skepticism directly. As outlined by
Nelson (2009), there are two primary conceptualizations of professional skepticism within the accounting
literature. These divergent conceptualizations have led to two distinct perspectives on how to measure
and evaluate trait professional skepticism. Nelson (2009) supports a presumptive doubt view of
professional skepticism in which an auditor demonstrating high professional skepticism needs relatively
more persuasive evidence to conclude an assertion is correct relative to the norm. In contrast, Hurtt
(2010) conceptualizes a neutral view of professional skepticism in which an auditor suspends judgment
until substantial evidence is obtained to reach a conclusion. She defines professional skepticism as a
multidimensional individual characteristic that can be categorized as both a trait and a state. Under this
conceptualization, Hurtt (2010) draws on prior research on skepticism from auditing, psychology,
philosophy, and consumer behavior literature in order to develop six individual characteristics that shape
an individual’s trait professional skepticism. These characteristics include a questioning mind, a
suspension of judgment, a search for knowledge, interpersonal understanding, self-esteem, and autonomy.
10
Using these characteristics as a guide, Hurtt (2010) develops a scale to measure an auditor’s trait
professional skepticism. Subsequent studies using Hurtt’s professional skepticism scale find evidence of
a link between an individual’s professional skepticism as measured using the Hurtt Scale and skeptical
judgments and actions (Quadackers et al. 2009; Hurtt et al. 2008).
While there is currently no direct measurement of an individual’s inherent presumptive doubt as
conceptualized by Nelson (2009), recent research operationalizes presumptive doubt as reduced
interpersonal trust and uses the Rotter Interpersonal Trust (RIT) scale from the psychology literature to
measure auditor trait professional skepticism (Quadackers et al. 2014). Results of this study indicate that
both the RIT scale and the Hurtt scale are equally predictive of auditor skeptical decisions in low risk
situations, but in high risk situations the RIT has greater impact on auditor skeptical judgment and
decisions (Quadackers et al. 2014). However that study does not capture the correct level of skeptical
actions the auditors should take in response to the risk present in the high risk case.
Furthermore, no research to date has considered how individuals with higher levels of trait
professional skepticism, as measured using either scale, behave in low risk settings relative to high risk
settings. It may be the case that while those scoring higher on the trait PS scales demonstrate more
skeptical judgments and actions in high risk situations, they fail to adequately adjust downwards their
judgments of the risk of fraud in lower risk scenarios. Thus, it may be the case that what the scales really
capture are those individuals who consistently judge the risk of fraud to be high even when the risk is
really quite low. To this end, these scales may capture auditor effectiveness at the expense of efficiency.
To the extent individual differences in trait professional skepticism produce differential outcomes in
terms of skeptical judgments and actions, the mix of individuals within a fraud brainstorming session may
have a significant impact on the quality of fraud risk assessments and responses generated by the session.
Hypothesis Development
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As previously stated, the accounting literature documents evidence of a link between an auditor’s
inherent level of trait professional skepticism (PS) and skeptical judgements and actions. The research
also finds fraud brainstorming can lead to increased audit quality through better identification of fraud
risk factors and hypotheses, and appropriate changes to fraud risk assessments. What is unclear, however,
is whether the trait PS of individuals within a brainstorming group can create differential outcomes in
brainstorming quality. The results of group brainstorming represent a synthesis of the ideas generated by
the individual members of the group. A primary benefit of brainstorming is posited to be the stimulation
and synergy created by the group dynamic. If auditors with elevated levels of trait PS are able to provide
incremental insight into the risk of fraud within a given engagement, then it seems reasonable that
targeted inclusion of these individuals in fraud brainstorming sessions could improve overall fraud
brainstorming quality. Indeed, consistent with this expectation, prior research documents that the
presence of minority viewpoints within a group can improve overall group performance (Wood et al.
1994; McLeod et al. 1997; De Dreu and West 2001).
Because auditors with higher levels of trait PS form more skeptical judgments relative to auditors
with lower levels of the trait (Hurtt et al. 2008, Quadackers et al. 2009), their inputs to brainstorming are
likely to be perceived as more extreme than their less skeptical counterparts in high risk situations. This
increased extremity represents a minority viewpoint. Prior research finds that the expression of minority
opinions within a group can stimulate divergent thinking and widen the scope of a group’s search for
solutions (McLeod et al 1997). Furthermore, the presence of a minority viewpoint can prevent premature
movement towards consensus and promote cognitive complexity (De Dreu & West 2001, Martin et al.
2007). In essence, the presence of a minority viewpoint within a group can encourage the group as a
whole to think more deeply about the matter being discussed. This deeper consideration is thought to not
only increase the amount of time spent contemplating the matter amongst group members, but also to
increase the diversity of factors considered (McLeod et al 1997). Thus, we suspect that brainstorming
groups that contain high trait PS auditors may perform better because their inclusion encourages the
12
group to develop a more complete set of potential fraud hypotheses. Furthermore, groups which identify
a greater number of fraud hypotheses are likely to evaluate the risk of fraud as higher (Hammersley,
Bamber, and Carpenter 2010). Stated formally, our first set of hypotheses assert:
H1: Brainstorming audit teams that include at least one individual with high trait
professional skepticism will produce higher fraud risk assessments than audit teams with
no individuals with high trait professional skepticism in high fraud risk situations.
H2: Brainstorming audit teams that include at least one individual with high trait
professional skepticism will identify more fraud hypotheses than teams with no
individuals with high trait professional skepticism in high fraud risk situations.
These hypotheses assume that social comparison processing does not influence the participation
behavior of high trait PS individuals within the group. If participants with high levels of trait PS engage
in social comparison processing, then these individuals may reduce the quality of their participation in the
group to match their less skeptical counterparts. Thus, if social comparison processing negatively affects
the behavior of individuals with high trait PS scores in groups when they are outnumbered by those with
low levels of trait PS, it is also possible that if the majority of the group does not possess high trait PS, the
potential cognitive stimulation gained by including an individual with high trait PS in the group may be
reduced or lost completely. However, if the synergistic properties of group brainstorming allow the
minority views of auditors with high trait PS to raise the skeptical awareness of the group, it is not clear
that every participant within a fraud brainstorming group must have high levels of trait PS in order to
produce the best brainstorming results. Given these alternative possibilities, we explore whether, given
that at least one group member has high trait PS, a higher number of group members with high trait PS
improves the quality of group brainstorming. To examine this relationship, we develop our first research
question:
RQ1: Given that at least on member in the group has a high trait PS score, does a
relationship exist between the number of group members with high levels of trait
professional skepticism and the quality of group brainstorming outcomes as measured by
the assessed risk of fraud and the quantity and quality of fraud hypotheses generated?
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While the effectiveness of fraud risk brainstorming is the primary focus of auditing standards,
efficiency is also an important concern for audit firms. Examination of the percentage of firms
investigated by the SEC indicates that the actual risk of fraudulent financial reporting among publicly
traded companies remains quite low, and determining the level of work to be performed during an audit
represents a tradeoff between reaching a sufficient level of assurance and controlling costs (Beasley,
Carcello, Hermanson, and Neal 2010). Given that the risk of fraud is in actually quite low, an auditor
must not only ensure that their audit program reaches a sufficient level of assurance, but must also control
costs in order retain clients.
If the synergistic properties of group brainstorming allow auditors with high trait PS to raise the
skeptical awareness of the group in high fraud risk situations, the question remains as to what, if any,
impact this might have on the group in lower fraud risk situations. If group brainstorming allows a single
individual with higher levels of trait PS to increase group fraud risk assessments in high risk settings, then
it seems possible this would lead to similar effects in lower fraud risk situations. However, auditors with
high trait PS may be able to appropriately differentiate between low and high fraud risk in their decision
making. If this is the case, then there would be no reason to suspect efficiency to be compromised in
lower fraud risk situations. However, little research to date considers the association between audit
efficiency and the level of an individual’s inherent trait PS.
To consider whether efficiency differences exist between groups that have members with high
levels of trait PS versus those that don't, we consider fraud risk brainstorming outcomes in a lower fraud
risk setting. If groups that include members with high trait PS become too skeptical due to the synergistic
nature of brainstorming, then these groups would tend to over-estimate the risk of fraud when fraud risk is
lower. Since it is difficult to predict what effect heightened trait PS will have in a lower fraud risk
situation, we pose a research question with no prediction:
RQ2: What impact does heightened individual trait professional skepticism of members
within a brainstorming group have on the efficiency of fraud risk brainstorming in lower
fraud risk settings?
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Figure 1 presents a graphical representation of the operationalization of our hypotheses and
research questions.
INSERT FIGURE 1 HERE
RESEARCH DESIGN
To test our research hypotheses and questions we employed a 3 (group type) X 2 (case type)
between-subjects design. The three types of brainstorming groups included three-person groups with no
members with high trait PS scores, groups with one member with a high trait PS score, and groups with
two members with high trait PS scores. The two levels of the case type included a higher fraud risk case
and a lower fraud risk case.
The two versions of the case were based on the Helecom Communications case developed by
Ballou and Mueller (2005b). The original case contains a detailed and realistic description of a client
organization with numerous fraud risk indicators related to both the client and the client’s industry. This
case was designed to be used in undergraduate and graduate auditing courses with an emphasis on fraud
risk assessments or assurance.
Participants
The participants were 252 Master of Accounting accounting students in eighty-four groups
consisting of three students per group. 4, 5 Accounting students are appropriate participants for this
experiment for several reasons. First, the study focuses on individual differences, or traits, and these have
4
The student participants were drawn from multiple advanced auditing courses at three major universities. Sixtyeight percent of the participants reported that they had completed at least one accounting-specific internship.
Furthermore, 46% reported completing an auditing specific internship. All students received the same instructions
concerning PCAOB AS 2401 and AS 2110 fraud risk brainstorming requirements prior to the experiment.
5
271 students participated in both days of the experiment. Of these participants, 255 students were assigned to a
group of 3 on the second day of the experiment. One group of 3 was later determined to be an outlier. The three
participants from this outlier group were excluded from all individual-level analyses in addition to the group level
analyses.
15
been shown to be relatively stable across time (Roberts & DelVecchio 2000). Second, while advanced
accounting students may lack auditing experience, they have gained from their academic studies
knowledge of the audit process, fraud risk, and the notion of professional skepticism. Thus, students
should have enough relevant knowledge about, and interest in, the subject matter to productively
participate in group brainstorming, especially given that the case was designed for audit students (Ballou
and Mueller 2005b). Furthermore, previous auditing studies which examine the relative performance of
groups in a fraud brainstorming task have utilized students as subjects using the same case study
employed in the current experiment (Lynch et al. 2009, Hartt 2014).
The experiment was conducted over two class periods with a one day break between classes. On
the first day of the experiment, PCAOB AS 2401 and PCOAB AS 2110 fraud risk brainstorming were
covered. Participants also completed the Hurtt Scale (Hurtt 2010) and the RIT Scale (see Quadackers
(2009)). They were given case materials to read before returning to class to complete the second phase of
the experiment. Participants were randomly assigned to receive one of the two case types (higher versus
lower risk) and were specifically instructed not to discuss the materials with anyone outside of class. The
participants were instructed to review the case materials prior to returning to complete the second phase
of the experiment and were made aware of the fact that the first task they would face upon returning
would be a quick five question quiz designed to test their familiarly with the details of the case. All
participants were offered extra credit for their participation in the study.
We computed participants’ trait PS scores using the Hurtt scale. The standardized mean score was
73.57 (s.d. 7.23), which is not significantly different than the average student score reported in Hurtt
(2010). 6 Previous research does not document a scale score that is indicative of high levels of trait PS, so
for the purposes of this study, those scoring at least half a standard deviation above the mean were
designated as having high levels of trait PS. While this represents an arbitrary cut point, it does provides
6
The unstandardized mean score was 132.43 and the standard deviation was 13.01. A two-sample t-test revealed an
insignificant difference between the average score obtained in this study and the original mean score obtained in
Hurtt (2010). There was also a non-significant difference in mean scores across students from the three universities
that comprise our sample population.
16
a more stringent definition than using a simple median split to separate participants into high and low
professional skepticism, and also enabled an appropriate split of participants into experimental groups. 7
Students were then randomly assigned to one of three group types using a stratified sampling technique.
The first group type, the “No PS” group was assigned no participants designated as high in trait PS. The
second group type, the “One-High PS” group was assigned one participant designated as high in trait PS.
The third group type, the “Two-High PS” group was assigned two participants designated as high in trait
PS. 8
Case Materials and Experimental Procedures
We constructed two versions of the case study used in this experiment based on the Helecom
Communications case developed by Ballou and Mueller (2005b). For the higher risk condition the
Helecom Communications case was used without any adjustments. However, adjustments were made to
the case for the lower risk condition. For the lower risk case, the narrative description of the firm was
adjusted to remove most of the client-specific indicators of high fraud risk. For example, in the higher
risk case participants were told that this was the first year of the engagement. In contrast, in the lower
risk case participants were informed that this was the fifth year of the engagement. As another example,
in the higher risk case participants were informed that the CEO was also the chairman of the board and
that the board was comprised of friends and family members of the CEO. This is contrasted with the
details of the lower risk case, in which participants were told that the chairman of the board was
completely independent from the CEO and that the board was comprised of several highly respected and
experienced individuals within the industry. Table 1 presents a list of the differences in risk
characteristics between the lower risk case and the higher risk case.
7
An analysis after the fact revealed that the cutoff for placement in the high PS category was in the 70th percentile of
the sample as a whole.
8
Because the data was collected over multiple courses, what constituted half a standard deviation above the mean
shifted slightly from our initial data collection to our final data collection. This affected the assignment of 3
participants between high and low professional skepticism, which in turn affected the group type for three
observations. These three observations were re-classified into the appropriate group type based on the final
calculation of our high skepticism cut-off score. Excluding these groups from the group level analyses does not
materially affect the interpretation of any of our results.
17
While the details of the case were altered in the lower risk case, the lengths of the descriptions
between the two versions were not materially different 9. Furthermore, the original case also included a
condensed set of comparative financial statements. The financial statements were not altered between the
two cases except for one footnote related to related-party transactions that was excluded from the lower
risk case. Other than the changes described here, the case materials and information provided in both
versions of the case were identical. 10 Furthermore, the participants were not made aware of the fact that
there were multiple versions of the case study, but were told that it was important not to discuss the case
between experimental sessions.
INSERT TABLE 1 HERE
271 students completed both phases of the experiment. 11 Three participants were assigned to
each group. This resulted in 84 useable group samples. See Table 2 for a breakdown of the number of
groups by experimental condition.
INSERT TABLE 2 HERE
At the beginning of the second phase of the study, participants were given a brief quiz. The quiz
questions addressed simple details of the case and were designed to ensure each participant had reviewed
the case before the beginning of the experiment. The average quiz score was a 97.98, indicating that
participants took the task seriously and had adequately reviewed the case. Stage 1 of the experiment
began immediately following the completion of the quiz.
9
The narrative descriptions of both cases were approximately 4.5 pages long.
The experimental instrument is available from the authors upon request.
11
Analyses completed at the individual level pre-brainstorming are based on 267 students who completed both
stages of the experiment and were not excluded for being either an outlier (3 observations) or for failing to complete
the initial individual fraud risk assessment (1 observation). Students who were not assigned to a 3 person group
brainstormed in smaller groups, but these observations were not included in the group level analyses.
10
18
In Stage 1, participants were given a new copy of the case along with an initial fraud risk
assessment form. They were given 15 minutes to review the case materials and complete the initial fraud
risk assessment. Participants were asked to separately evaluate the significance of the fraud risk, the
pervasiveness of the fraud risk, and the likelihood of the risk using a series of 10-point Likert scales. 12 At
the completion of Stage 1, each participant’s initial fraud risk assessment was collected by the
experimenter, however each participant was allowed to keep his copy of the case materials.
In Stage 2 of the experiment the participants were given their group assignments and asked to
relocate to their designated group area. One member of each group was randomly assigned to act as the
group’s recorder. Each group was instructed to brainstorm as a team to develop a list of fraud hypotheses
and complete a group fraud risk assessment. The groups were given 30 minutes to complete this phase of
the experiment. Following the completion of the brainstorming session, all materials were collected from
each group.
At the conclusion of Stage 2, participants returned to their original seats to provide a final fraud
risk assessment. Participants were told that they should not complete this task based on their initial
individual responses or their group’s responses, but rather they should complete the risk assessment based
on their current personal judgments. Participants concluded the study by completing an exit
questionnaire.
Dependent Measures and Data Coding
We calculated dependent measures at the group level from the group fraud risk assessments and
the fraud hypotheses each group generated. Dependent measures are (1) the assessed risk of fraud, and
(2) the number of unique fraud hypotheses generated. To collect each group’s fraud risk assessment, we
used the same series of 10-point Likert scale questions from the individual initial fraud risk assessments.
To measure the total number of unique fraud hypotheses generated by each group, two coauthors
with prior audit practice experience who were blind to the group condition independently coded each item
12
See PCAOB AS 2110.59 for a discussion of the three attributes of risk.
19
in both the higher and lower risk case conditions. Coders used a case solution based on the teaching notes
provided by Ballou and Mueller (2005a) as a guide in order to determine whether each item recorded by a
group constituted a fraud hypothesis. Cohen’s kappas for both the higher and lower risk conditions
(kappa = 0.89, 0.94) indicates a high degree of initial inter-rater reliability. Once initial independent
coding was complete, all differences were reconciled between the two coders.
RESULTS
Preliminary Analyses
To determine whether our manipulations succeeded, we first examined whether the case study
condition (higher versus lower risk), and trait PS cutoff (high PS versus low PS) affected the level of
assessed fraud risk in the participants’ initial individual fraud risk assessments. The individual participant
summary statistics are reported in Panel A of Table 3. We utilized a 2X2 full-factorial MANCOVA to
evaluate whether the level of assessed fraud risk for the three attributes of risk (likelihood, significance,
pervasiveness) significantly differed across case type (higher or lower risk) and trait PS level (high versus
low PS). 13 The results are reported in Panel B of Table 3 and provide strong evidence that the case study
manipulation (higher versus lower risk) succeeded. Specifically, the main effect of case type was
statistically significant (F3, 259 = 10.67, p < 0.01) and all follow-up univariate ANOVAs for each attribute
of risk were significant at the p <0.05 level. We further observed a significant main effect for PS level
(F3, 259 = 3.85, p = 0.01) and again all follow-up univariate ANOVAs for each attribute of risk were
statistically significant at the p < 0.05 level. 14 High trait PS participants evaluated the risk of fraud as
higher than participants with lower levels of trait PS. The interaction was non-significant indicating that
the relationship between the assessed risk of fraud for the three attributes of risk and trait PS level does
not significantly differ across case conditions.
13
School (one of three universities) is included as a covariate in all analyses.
The F-stats and p-values reported in the results section for all MANCOVA analyses are based on Pillai-Bartlett
Trace as it tends to be the most conservative test statistic.
14
20
INSERT TABLE 3 HERE
Primary Analyses
Our first hypothesis predicted that brainstorming audit teams that included at least one individual
with high trait PS would assess the risk of fraud as higher in the higher risk case relative to groups that
included no high trait PS participants. To address this hypothesis we employed a 3X2 full-factorial
MANCOVA to examine the effects of group type (No High PS, One High PS, Two High PS) and case
type (lower versus higher risk) on group risk assessments for the three attributes of risk. 15 The results of
this analysis are reported in Panel B of Table 4. An examination of the MANCOVA indicates a
significant main effect for case type (F3,74 = 2.94, p = 0.04), and a significant main effect for group (F6, 150
= 2.31, p = 0.04) with a non-significant interaction. To address H1, we used a planned contrast to
examine whether groups which included at least one high PS participant evaluated the risk of fraud as
higher than groups which did not contain at least one high PS participant. The results of this analysis are
presented in Panel C of Table 4. The multivariate planned contrast was statistically significant (F3, 74 =
3.50, p = 0.02) as were all of the follow-up univariate ANOVAs. Thus, consistent with H1, including at
least one high PS participant in the group significantly increases the group’s perceived risk of fraud.
INSERT TABLE 4 HERE
Our second hypothesis predicted that groups with at least one high trait PS member would
identify more fraud hypotheses, and more relevant hypotheses, relative to groups with no high PS
participants in the higher risk case. We utilized a Poisson binomial regression to address this hypothesis
in which the dependent variable was total number of unique hypotheses generated by the group. The
independent variables of interest was group type (No High PS, At Least One High PS) and case condition
15
The group level summary statistics for the group risk assessments are reported in Panel A of Table 4.
21
(lower risk, higher risk). The results of these analyses are reported in Table 5. An examination of the
regression in Panel B of Table 5 indicates that while the regression coefficient was in the expected
direction, the result was not statistically significant (Coef. = 0.17, p = 0.22). Inconsistent with H2, groups
that contained at least one high PS participant did not identify more unique fraud hypotheses relative to
groups which contained no high PS participants. These data indicate that while including high PS
participants in the group increases the group’s perceived risk of fraud, it does not translate into an
improved ability to identify specific fraud hypotheses.
INSERT TABLE 5 HERE
Research Question 1 addressed whether a relationship exists between the number of high trait PS
members within a brainstorming group and the outcomes of the group’s brainstorming session. To
address this research question we return to the 3X2 group level MANCOVA presented in Table 4 and use
a planned contrast to examine whether there are significant differences in fraud risk assessments across
groups that contained only one high PS participant and groups that contained two high PS participants.
The results of this analysis are presented in Panel E of Table 4. The results of the multivariate planned
contrast indicate a non-significant difference between these two group types (F3, 74 = 1.15, p = 0.33).
These data indicate that it only requires the targeted inclusion of at least one high PS participant in the
group to significantly increase the skeptical awareness of the group as a whole, and the inclusion of
additional high PS members has no marginal benefit.
Our second research question speaks to whether there are any significant efficiency differences
between groups containing high trait PS members and those that do not in lower risk situations. To
address this research question we returned to our fraud risk assessment 3X2 group level MANCOVA.
Because there was a main effect for both the case condition, and the group condition, but no significant
interaction, we conclude that including high PS participants in the group does not appear to cause the
22
group to systematically over-estimate the risk of fraud. That is, while groups which contain high PS
participants appear to systematically evaluate the risk of fraud as higher on average, it appears that the
groups are still able distinguish between high and low risk situations.
Supplemental Analyses
Best Member Analysis
Previous research in the group decision-making literature finds consistent evidence that the
group’s ability to identify the best member within the group is a significant predictor of group
performance (Libby, Trotman, & Zimmer, 1987). This suggests that a potential driver of a relationship
between the inclusion of high PS participants and higher levels of brainstorming quality is driven by the
group’s ability to accurately identify the member with the highest level of trait professional skepticism.
To investigate this possibility, we examined the frequency with which participants chose high trait PS
group members as the best member of the group.
In the post-experimental questionnaire, we asked participants to identify the best member of their
group. We then focused on the One-High PS groups and used mean proportion testing to examine
whether the high trait PS person was statistically more likely to be chosen as the best participant. The
high trait PS participant was chosen by group members in the One-High PS group as the best member of
the group 62% of the time, which is significantly higher than what would be expected based on chance
alone (z = 4.98, p < 0.01). We further broke it down by case type to see if there was a significant
difference between the likelihood that the high trait PS person was chosen as the best member of the
group for both the higher and lower risk case. The high PS participant was significantly more likely to be
chosen as the best member of the group regardless of whether the group was in the higher risk case
(z=2.63, p = 0.01) or the lower risk case (z = 4.44, p < 0.01). Lastly, we broke this analysis down by case
type and PS level to examine whether participants with high or low levels of professional skepticism
varied in their assessment of who was the best member of the group. The results indicate that participants
23
routinely evaluated the high PS participant as the best member of the group (z stats ranged from 3.921.84, p value from <0.01 – 0.07).
Effect of Skeptical Groups on Auditors with Low Trait Skepticism
Another potential benefit of brainstorming is that it may enable auditors with lower levels of PS
to more effectively evaluate risk on an individual basis post-brainstorming. Because we found that group
members identified the more highly skeptical members as the best members, and highly skeptical
members had higher initial risk evaluations, we focus our analysis of post-brainstorming risk assessment
on low PS participants. Table 6 presents the effects of group type on the final fraud risk assessments of
low PS participants.
INSERT TABLE 6 HERE
Table 6 demonstrates that although low PS participants continue to assess the high-risk scenario
as being of greater risk than the low-risk scenario (F3,163 = 6.40, p < 0.01), being grouped with a high PS
participant did not incrementally increase the final risk assessments of low PS participants. As shown in
Table 6, low PS participants who were in a brainstorming group with at least one high PS participant did
not make final fraud risk assessments that were statistically different from low PS participants without a
high PS group member (F3,163 = 1.61, p = 0.20), nor did this vary by case type (F3,163 = 0.82, p = 0.48).
Thus, even though participants identified high PS participants as the best members, and groups with high
PS participants assessed fraud risk as higher, this effect did not carry over for low PS participants postbrainstrorming.
SUMMARY AND CONCLUSIONS
The purpose of this study was to examine how individual differences in trait professional
skepticism impact group fraud risk brainstorming. The consistent application of professional skepticism
24
throughout the audit process continues to be of utmost concern to the PCAOB and audit firms. By
providing insight into how individual differences in professional skepticism affect the outcomes of group
brainstorming, this study helps to address these concerns.
The results of this study indicate that individual differences in trait professional skepticism of
group members can significantly impact the outcomes of fraud risk brainstorming at least in terms of the
group’s perceptions of fraud risk. In both high and low risk situations, groups that contain at least one
member with high trait professional skepticism evaluate the overall risk of fraud higher than groups that
do not contain any individuals with high trait professional skepticism. However, a significant difference
in risk perceptions for these groups across high and low risk conditions indicates that the targeted
inclusion of these high PS participants does not negatively impact the efficiency of the audit by causing
the group to systematically over-estimate the risk of fraud. These results suggest that the synergistic
properties of brainstorming make it possible for those with high levels of professional skepticism to
positively influence the skeptical awareness of the group. However, while the targeted inclusion of high
PS participants in the group does appear to systematically raise the skeptical awareness of the group, it
does not improve the group’s ability to identify relevant underlying fraud hypotheses. Further, low PS
participants’ final individual fraud risk assessments were not affected by the presence of high PS
participants in their brainstorming groups. Thus, further research is needed to determine the influence of
high PS auditors on post-brainstorming outcomes.
The findings presented in our study contribute not only the accounting literature, but also have
implications for practice. Regulators, practitioners, and academicians generally focus on the individual
when considering professional skepticism. However, our study examines the possibility that within a
group of auditors, not all auditors must possess high inherent levels of professional skepticism in order for
the group as a whole to make more skeptical judgements.
This study is subject to several limitations. First, we relied on a measured variable to capture
individual levels of trait professional skepticism. While previous research documents the relation
25
between higher levels of trait PS as measured by the Hurtt scale and higher levels of assessed risk,
previous research has not identified an appropriate cut point for identifying those with high professional
skepticism. As such, we used a half a standard deviation above the mean to identify individuals with high
levels of trait professional skepticism. There is no guarantee that this cut point truly captures individuals
who should be labeled high in trait professional skepticism.
We used students with classroom audit experience as our subjects. Because the case we used was
designed for students and has been used in prior research that addresses fraud risk brainstorming (Lynch
et al. 2009, Hartt 2014), we believe this design choice is appropriate. Furthermore, recent research
indicates that advanced audit students may be a good proxy for first year auditors (Bennett and Hatfield
2013). However, to the extent that the relation between trait skepticism interacts with experience in
influencing fraud risk evaluations, it is possible that the phenomena documented here would not
generalize to a more natural setting. Future research is needed which examines the relationship between
trait skepticism and experience and its potential effect on group-level performance.
Our study represents a first step in examining how individual-level characteristics such as trait
professional skepticism can influence group-level performance during fraud risk brainstorming. Because
we were interested in how individual levels of trait PS might impact the group as a whole, we elected to
assemble brainstorming groups that were not hierarchical in nature. This choice gave us the ability to
examine this relationship in isolation of a group hierarchy. However, additional research is needed in this
area to understand the potential interplay between individual levels of trait PS and group performance in
hierarchical groups. For example, if audit seniors are susceptible to social loafing in electronic
brainstorming groups (Chen et al. 2014); does higher levels of individual trait PS help to counteract this
tendency? Furthermore, what influence does the trait PS level of more senior members in the group have
on the group’s performance? We believe our research provides a foundation for exploring these
important topics in future research.
26
We adapted a published case study to use as the instrument in this case. Our inferences from the
data concerning the effectiveness and efficiency of these groups during brainstorming are based on the
differences between these groups. However, they do not capture the difference between the group’s
assessments and an expert’s assessment of the risk of fraud. Future research should explore in more detail
the notion of auditor efficiency and the potential downside of consistently focusing on skeptical
judgments and actions.
27
REFERENCES
“AS 2110: Identifying and Assessing Risks of Material Misstatement.” 2010. Public Company
Accounting Oversight Board. https://pcaobus.org/Standards/Auditing/Pages/AS2110.aspx.
“AS 2401: Consideration of Fraud in a Financial Statement Audit.” 2002. Public Company Accounting
Oversight Board. https://pcaobus.org/Standards/Auditing/Pages/AS2401.aspx.
“AS 1015: Due Professional Care in the Performance of Work.” 1997. Public Company Accounting
Oversight Board. https://pcaobus.org/Standards/Auditing/Pages/AS1015.aspx.
Ballou, Brian, and Jennifer M. Mueller. 2005a. Teaching Notes: Helecom Communications: Considering
Fraud Risk on an Audit Engagement before and after Analyzing a Key Business Process. Issues
in Accounting Education 20 (1).
———. 2005b. Helecom Communications: Considering Fraud Risk on an Engagement before and after
Analyzing a Key Business Process. Issues in Accounting Education 20 (1): 99–118.
doi:10.2308/iace.2005.20.1.99.
Baumann, Martin, Chief Auditor. 2012. Remarks Concerning PCAOB Developments presented at the
AICPA Conference on Current SEC and PCAOB Developments, December 4, Washington, DC.
http://pcaobus.org/News/Speech/Pages/12042012_AICPA.aspx.
Beasley, Mark, Joseph Carcello, Dana Hermanson, and Terry Neal. 2010. Fraudulent Financial
Reporting 1998-2007: An analysis of U.S. Public Companies. Committee of Sponsoring
Organizations of the Treadway Commission.
Bennett, Bradley, and Richard Hatfield. 2012. The Effect of the Social Mismatch Between Staff Auditors
and Client Management on the Collection of Audit Evidence. The Accounting Review 88 (1): 3150.
Brazel, Joseph F., Tina D. Carpenter, and J. Gregory Jenkins. 2010. Auditors’ Use of Brainstorming in
the Consideration of Fraud: Reports from the Field. The Accounting Review 85 (4): 1273–1301.
Camacho, L. Mabel, and Paul B. Paulus. 1995. The Role of Social Anxiousness in Group Brainstorming.
Journal of Personality and Social Psychology 68 (6): 1071–80.
Carpenter, Tina D. 2007. Audit Team Brainstorming, Fraud Risk Identification, and Fraud Risk
Assessment: Implications of SAS No. 99. The Accounting Review 82 (5): 1119–40.
Carpenter, Tina D., and Jane L. Reimers. 2013. Professional Skepticism: The Effects of a Partner’s
Influence and the Presence of Fraud on Auditors’ Fraud Judgments and Actions. Behavioral
Research in Accounting 25 (2): 45-69.
Cicchetti, Domenic V. 1994. Guidelines, Criteria, and Rules of Thumb for Evaluating Normed and
Standardized Assessment Instruments in Psychology. Psychological Assessment 6 (4): 284–90.
De Dreu Carsten K. W., Michael A. West. 2001. Minority Dissent and Team Innovation: The
Importance of Participation in Decision Making. Journal of Applied Psychology 86 (6): 11911201.
Dugosh, Karen Leggett, Paul B. Paulus, Evelyn J. Roland, and Huei-Chuan Yang. 2000. Cognitive
Stimulation in Brainstorming. Journal of Personality and Social Psychology 79 (5): 722–35.
Franzel, Jeanentte. 2013. Protecting Investors by Seizing the Opportunity to Strengthen Audit Quality
presented at the American Accounting Association Midyear Conference and Doctoral
Consortium, January 18, New Orleans, LA.
28
Hallgren, Kevin. 2012. Computing Inter-Rater Reliability for Observational Data: An Overview and
Tutorial. Tutor Quant Methods Psychology 8 (1): 23–34.
Hammersley, Jacqueline S., E. Michael Bamber, Tina D. Carpenter. 2010. The Influence of
Documentation Specificity and Priming on Auditors’ Fraud Risk Assessments and Evidence
Evaluation Decisions. The Accounting Review 85 (2): 547-571.
Hammersley, Jacqueline S., Karla Johnstone, and Kathryn Kadous. 2011. A Review and Model of
Auditor Judgments in Fraud-related Planning Tasks. Auditing: A Journal of Practice & Theory
30.4: 101-128.
Hammersley, Jacqueline S. 2011. How do Audit Seniors Respond to Heightened Fraud Risk? Auditing:
A Journal of Practice and Theory 30 (3): 81-101.
Harding, Noel, and Ken Trotman. 2011. Enhancing Professional Skepticism via the Fraud Brainstorming
Discussion Outcomes. Working Paper.
Hartt, Allen. 2014. The Impact of Collective Intelligence on the Fraud Brainstorming Effectiveness of
Traditional and Virtual Audit Groups. Working Paper.
Hoffman, Vicky B., and Mark F. Zimbelman. 2009. Do Strategic Reasoning and Brainstorming Help
Auditors Change Their Standard Audit Procedures in Response to Fraud Risk? The Accounting
Review 84 (3): 811–37.
Hooghe, Marc, Sofie Marien, and Thomas de Vroome. 2012. The Cognitive Basis of Trust. The Relation
Between Education, Cognitive ability, and Generalized and Political Trust. Intelligence 40 (6):
604-613.
Hurtt, Kathy. 2010. Development of a Scale to Measure Professional Skepticism. AUDITING: A Journal
of Practice & Theory 29 (1): 149–71.
Hurtt, Kathy, Martha Eining, and David Plumlee. 2008. An Experimental Examination of Professional
Skepticism. Working Paper.
Hurtt, R. Kathy, Helen L. Brown-Liburd, Christine E. Earley, and Ganesh Krishnamoorthy. 2013.
Research on Auditor Professional Skepticism- Literature Synthesis and Opportunities for Future
Research. AUDITING: A Journal of Practice & Theory 32 (Sp. 1): 45-97.
Laidra, Kaia, Helle Pullmann, Juri Allik. 2007. Personality and Intelligence as Predictors of Academic
Achievement: A Cross-sectional Study from Elementary to Secondary School. Personality and
Individual Differences 42: 441-451.
Libby, R., Trotman, K. T., & Zimmer, I. 1987. Member Variation, Recognition of Expertise, and Group
Performance. Journal of Applied Psychology, 72 (1): 81–87.
Lounsbury, John, Eric Sundstrom, James Loveland, and Lucy Gibson. 2003. Intelligence, "Big Five"
Personality Traits, and Work Drive as Predictors of Course Grade. Personality and Individual
Differences 35: 1231-1299.
Lynch, Antoinette L., Uday S. Murthy, and Terry J. Engle. 2009. Fraud Brainstorming Using Computer‐
Mediated Communication: The Effects of Brainstorming Technique and Facilitation. The
Accounting Review 84 (4): 1209–32.
Martin, Robin, Pearl Y. Martin, Joanne R. Smith, and Miles Hewstone. 2007. Majority versus Minority
Influence and Prediction of Behavioral Intentions and Behavior. Journal of Experimental Social
Psychology 43: 763-771.
McGraw, Kenneth O., and S. P. Wong. 1996. Forming Inferences about Some Intraclass Correlation
Coefficients. Psychological Methods 1 (1): 30–46.
29
McLeod, Poppy L., Robert S. Baron, Mollie Weighner Marti, and Kuh Yoon. 1997. The Eyes Have It:
Minority Influence in Face-to-Face and Computer Mediated Group Discussion. Journal of
Applied Psychology 82 (5):706-718.
McMillan, Jeffrey J., and Richard A. White. 1993. Auditors’ Belief Revisions and Evidence Search: The
Effect of Hypothesis Frame, Confirmation Bias, and Professional Skepticism. The Accounting
Review 68 (3): 443–65.
Morris, CPA, PhD, Jan, and William Thomas, CPA, PhD. 2011. Clarified Auditing Standards: The Quiet
Revolution. Journal of Accountancy, June.
http://www.journalofaccountancy.com/issues/2011/jun/20113792.
Nelson, Mark W. 2009. A Model and Literature Review of Professional Skepticism in Auditing.
AUDITING: A Journal of Practice & Theory 28 (2): 1–34.
Observations on Auditors’ Implementation of PCAOB Standards Relating to Auditors’ Responsibilities
with Respect to Fraud. 2007. PCAOB Release No. 2007-001.
Paulus, Paul B., and Mary T. Dzindolet. 1993. Social Influence Processes in Group Brainstorming.
Journal of Personality and Social Psychology 64 (4): 575-586.
Peecher, Mark E., and Ira Solomon. 2001. Theory and Experimentation in Studies of Audit Judgments
and Decisions: Avoiding Common Research Traps. International Journal of Auditing 5 (3): 193–
203.
Quadackers, Luc, Tom Groot, and Arnold Wright. 2009. Auditor’s Skeptical Characteristics and Their
Relationship to Skeptical Judgments and Decisions. Working Paper.
———. 2014. Auditors’ Professional Skepticism: Neutrality versus Presumptive Doubt. Forthcoming in
Contemporary Accounting Review.
Roberts, Brent, and Wendy DelVecchio. 2000. The Rank-order Consistency of Personality Traits from
Childhood to Old Age: a Quantitative Review of Longitudinal Studies. Psychological bulletin
126 (1).
Sturgis, Patrick, Sanna Read, and Nick Allum. 2010. Does Intelligence Foster Generalized Trust? An
Empirical Test using the UK Birth Cohort Studies. Intelligence 38 (1) 45-54.
Trotman, Ken T., Roger Simnett, and Amna Khalifa. 2009. Impact of the Type of Audit Team
Discussions on Auditors' Generation of Material Frauds. Contemporary Accounting Research 26
(4): 1115-1142.
Wood, Wendy, Sharon Lundgren, Judith A. Oullette, Shelly Busceme, and Tamela Blackstone. 1994.
Minority Influence: A Meta-analytic Review of Social Influence Processes. Psychological
Bulletin 115 (3): 323-345.
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Table 1
Differences in Fraud Risk Indicators
High Risk Case
Family owned company until the public offering,
and little to no change in the culture after becoming
a public company.
CEO is also the chairman of the board.
Family members/managers have little regard for
other shareholders, believing that the company
belongs to them.
Board of directors consists of management and
friends of the family, reducing the ability of
independence of mental attitudes.
Voting power is concentrated among family
members.
Privately owned businesses established by Helecom
CEO to subsidize income, each of which generate
primary revenue streams from Helecom, based on
below-market prices charged to Helecom.
Purchase of Neo was justified as a project for
George and structured so that Neo is not a
subsidiary of Helecom
The accounting firm is providing a first-year audit
Low PS Group
Mid PS Group
High PS Group
Lower Risk Case
Significant change in the culture after going
public as the CEO worked hard to ensure
investors were comfortable with the company's
oversight.
Chairman of the board is independent of the
CEO position.
Significant focus on ensuring investors are well
informed and company decisions are in the best
interests of investors.
Board of directors is comprised of highly
respected and experienced individuals from the
industry.
Equal voting power between all investors.
No related party transactions.
Purchase of Neo wireless was approved by the
board and was structured as a subsidiary of
Helecom.
This is the 5th year the accounting firm has
provided the audit.
Table 2
Observations by Cell
Low Fraud Risk
16
13
15
31
High Fraud Risk
15
14
11
Figure 1:
Graphical Representation of Hypotheses and Research Questions
H1:Brainstorming audit teams that include at least one individual with high trait
professional skepticism will produce higher fraud risk assessments than audit teams
with no individuals with high trait professional skepticism in high fraud risk situations.
H2: Brainstorming audit teams which include at least one individual with high
trait professional skepticism will identify more fraud hypotheses, and more
relevant hypotheses, than audit teams with no individuals with high trait
professional skepticism in high fraud risk situations.
INDEPENDENT
DEPENDENT
INDEPENDENT
DEPENDENT
Concept Definion
Concept Definition
Concept Definion
Concept Definition
Brainstorming quality
Influence of individuals with
high levels of professional
skepticism
Influence of individuals with high
levels of professional skepticism
1
3
2
Operational Definition
Inclusion of at least one member
rated high in PS versus groups
with no members rated high in PS
4
1
3
Operational Definitions
Risk assessment score:
* Likelihood of the risk
* Significance of the risk
* Pervasiveness of the risk
2
Operational Definition
Inclusion of at least one
member rated high in PS versus
groups with no members rated
high in PS
RQ1: Given that at least one member in the group has a high trait PS score, does a
relationship exist between the number of group members with high levels of trait
professional skepticism and the quality of group brainstorming outcomes as
measured by the assessed risk of fraud and the quantity and quality of fraud
hypotheses generated?
Brainstorming quality
Operational Definitions
4
1) Total number of unique fraud
hypotheses identified by the group
2) Total number of relevant fraud
hypotheses identified by the group
RQ2: What impact does heightened individual trait professional skepticism of
members within a brainstorming group have on the efficiency of fraud risk
brainstorming in lower fraud risk settings?
INDEPENDENT
DEPENDENT
INDEPENDENT
DEPENDENT
Concept Definion
Concept Definition
Concept Definion
Concept Definition
Brainstorming quality
Influence of individuals with
high levels of professional
skepticism
Influence of individuals with high
levels of professional skepticism
1
3
2
Operational Definition
Number of members rated high in
PS included in the group
3
Operational Definitions
4
1
2
Operational Definition
1) Risk assessment score:
* Likelihood of the risk
* Significance of the risk
* Pervasiveness of the risk
2) Total number of unique fraud
hypotheses identified by the group
3) Total number of relevant fraud
hypotheses identified by the group
1) Inclusion of at least one
member rated high in trait
professional skepticism
2) Number of members rated
high in PS included in the group
32
Brainstorming efficiency
Operational Definitions
4
Risk assessment score:
* Likelihood of the risk
* Significance of the risk
* Pervasiveness of the risk
TABLE 3
Manipulation Check – Initial Individual Participant Analyses
Panel A: Descriptive Statistics: Marginal Means, Standard Error, Number of Observationsa
High Professional Skepticism
Low Professional Skepticism
Mean
Std. Er.
Obs.
Mean
Std. Er.
Obs.
Higher Risk
Likelihood of Risk
7.74
0.25
37
6.95
0.17
90
Significance of Risk
7.69
0.26
37
7.06
0.18
90
Pervasiveness of Risk
7.34
0.25
37
6.71
0.17
90
Lower Risk
Likelihood of Risk
6.40
0.23
45
6.19
0.16
95
Significance of Risk
7.18
0.24
45
6.60
0.17
95
Pervasiveness of Risk
6.54
0.23
45
6.22
0.16
95
Panel B: MANCOVA Attributes of Riskb, c
Error
Value
df
F
p
Source
df
School
0.05
6
520
2.43
0.03
Case Type
0.11
3
259
10.67
<0.01
Professional Skepticism
0.04
3
259
3.85
0.01
Case Type X Professional Skepticism
0.01
3
259
0.98
0.40
d
Panel C: Simple Effect of Case Condition
Cont.
Std.
Error
df
F
p
Source
Est.
Er.
df
Likelihood of the Risk
1.06
0.19
1
261
31.12
<0.01
Significance of the Risk
0.49
0.20
1
261
6.02
0.02
Pervasiveness of the Risk
0.65
0.19
1
261
11.32
<0.01
Panel D: Simple Effect of Professional Skepticism Levele
Cont.
Std.
Error
df
F
p
Source
Est.
Er.
df
Likelihood of the Risk
0.50
0.19
1
261
6.94
0.01
Significance of the Risk
0.61
0.20
1
261
9.31
<0.01
Pervasiveness of the Risk
0.48
0.19
1
261
6.19
0.01
These data represent the average assessed risk of fraud reported by participants in their initial fraud risk
assessments in Stage 1 of the experiment on day 2. All fraud risk assessments were measured using a
10-point Likert scale. The significance of the risk, the pervasiveness of the risk, and the likelihood of
the risk represent attributes of risk as outlined in PCAOB AU Sec. 316, paragraph 40.
a. These data represent the marginal means for high and low PS participants separated by case type.
b. This analysis presents a 2X2 full factorial MANCOVA where the outcome variables are the three
attributes of risk and the predictor variables are PS level and Case Type. School is included in the
analysis as a covariate.
c. The reported test statistics are based on Pillai-Bartlett trace as it tends to be the most conservative.
d. This analysis represents the simple effect of case and the test statistics are based on univariate
ANOVAs. The reference group in this analysis is the low risk case.
e. This analysis represents the simple effect of professional skepticism level and the test statistics are
based on univariate ANOVAs. The reference group in this analysis is low professional skepticism.
33
TABLE 4
Group Level Analyses for Attributes of Risk
Panel A: Descriptive Statistics: Marginal Means, Standard Error, Observationsa
No High PS Groups
One High PS Groups Two High PS Groups
Mean
SE
Obs. Mean
SE
Obs. Mean
SE
Obs.
Higher Risk
Likelihood of Risk
7.43
0.29
15
7.77
0.31
14
8.50
0.34
11
Significance of Risk
7.86
0.27
15
8.07
0.29
14
8.29
0.31
11
Pervasiveness of Risk
7.91
0.30
15
8.32
0.32
14
8.13
0.34
11
Lower Risk
Likelihood of Risk
6.92
0.28
16
7.62
0.32
13
7.45
0.29
15
Significance of Risk
7.52
0.25
16
8.21
0.29
13
7.93
0.27
15
Pervasiveness of Risk
7.10
0.28
16
8.18
0.33
13
7.58
0.30
15
Panel B: MANCOVA Attributes of Riskb, c
Error
Value
df
F
p
Source
df
School
0.19
6
150
2.63
0.02
Case Type
0.11
3
74
2.94
0.04
Group Type
0.17
6
150
2.31
0.04
Case Type X Group Type
0.05
6
150
0.64
0.70
d
Panel C: Simple Effect of Case Type
Cont.
Std.
Error
df
F
p
Source
Est.
Er.
df
Likelihood of the Risk
0.58
0.23
1
76
6.22
0.02
Significance of the Risk
0.19
0.21
1
76
0.76
0.39
Pervasiveness of the Risk
0.50
0.24
1
76
4.44
0.04
Panel D: Planned Contrast No High PS Groups vs Groups with High PS Participantse
Multivariate Planned Contrast
Error
Value
df
F
p
Source
df
No High PS Groups vs Other Groups
0.12
3
74
3.50
0.02
Follow-up Univariate ANOVAs
Cont.
Std.
Error
df
F
p
Source
Est.
Er.
df
Likelihood of the Risk
0.66
0.24
1
76
7.43
0.01
Significance of the Risk
0.44
0.22
1
76
3.84
0.05
Pervasiveness of the Risk
0.55
0.25
1
76
4.86
0.03
f
Panel E: Planned Contrast One High PS Groups vs Two High PS Groups
Multivariate Planned Contrast
Error
Value
df
F
p
Source
df
One High PS vs Two High PS Groups
0.05
3
74
1.15
0.33
Follow-up Univariate ANOVAs
Cont.
Std.
Error
df
F
P
Source
Est.
Er.
df
Likelihood of the Risk
0.29
0.34
1
76
0.92
0.34
Significance of the Risk
-0.03
0.27
1
76
0.01
0.92
Pervasiveness of the Risk
-0.39
0.30
1
76
1.67
0.20
These data represent the average assessed risk of fraud reported by groups during the brainstorming session. All
fraud risk assessments were measured using a 10-point Likert scale. The significance of the risk, the
34
pervasiveness of the risk, and the likelihood of the risk represent attributes of risk as outlined in PCAOB AU Sec.
316, paragraph 40.
a. These data represent the marginal means for groups separated by case type.
b. This analysis presents a 3X2 full factorial MANCOVA where the outcome variables are the three
attributes of risk and the predictor variables are Group Type and Case Type. School is included in the
analysis as a covariate.
c. The reported test statistics are based on Pillai-Bartlett trace as it tends to be the most conservative.
d. This analysis presents the simple effect of Case Type using follow-up univariate ANOVAs. The
outcome variables for this analysis are the three attributes of risk. The low risk case is the comparison
group in this analysis, positive contrast estimates indicate groups in the high risk case evaluated the risk
of fraud to be higher than groups in the low risk case.
e. This analysis presents the planned contrast of the No High PS Groups versus groups that contain at
least one high PS participant. The outcome variables for this analysis are the three attributes of risk.
The multivariate planned contrast is presented first, followed by univariate ANOVAs for each attribute
of risk. The No High PS group is the comparison group in this analysis, positive contrast values
indicate that groups with at least one high PS participant evaluated the fraud risk to be higher than No
High PS groups.
f. This analysis presents the planned contrast of One High PS Groups versus Two High PS Groups.
The outcome variables for this analysis are the three attributes of risk. The multivariate planned
contrast is presented first, followed by univariate ANOVAs for each attribute of risk. The One High
PS group is the comparison group in this analysis, a positive contrast value would indicate that groups
with two high PS participants evaluated the fraud risk to be higher than groups with one high PS
participant.
35
TABLE 5
Group Level Analyses for Fraud Hypotheses
Panel A: Descriptive Statistics: Means, Standard Error, Observationsa
No High PS Groups
At least One High PS Groups
Mean.
Std. Er.
Obs.
Mean
Std. Er.
Obs.
Higher Risk
2.80
2.18
15
2.32
1.65
25
Lower Risk
2.50
1.67
16
2.66
1.90
29
Panel B: Regression Unique Fraud Hypothesesb
Model Statistics
Wald Chi2(4 df) = 13.47, p = 0.01
Pseudo R2 = 0.04
Source
Coef.
Std. Er.
z
P
Intercept
0.54
0.18
3.11
<0.01
School
0.32
0.09
3.64
<0.01
Case Type
-0.14
0.17
-0.81
0.42
No High PS Group
-0.16
0.20
-0.79
0.43
Case Type X No High PS Group
0.29
0.28
1.03
0.30
These data represent the average number of unique fraud hypotheses identified by groups in both the
high and the low risk case. A fraud hypothesis is defined as a group idea that identifies both a method
management might use to commit fraud, and the corresponding accounts or classes of transactions that
would be affected.
a. These data represent the means for groups separated by groups with no high PS participants and
groups with at least one high PS participant in the group.
b. This analysis presents a Poisson regression where the outcome variable is the total number of unique
fraud hypotheses identified by the group and the predictor variables are case type and No High PS
Group. School is included in the analysis as a covariate. A Poisson regression was used instead of a
negative binomial regression because the LR test of alpha = 0, was not statistically significant (Chibar2(1 df) = 0.58, p = 0.22).
36
TABLE 6
Effects of Group Membership on Final Fraud Risk Assessments for Low PS Participants
Panel A: Descriptive Statistics: Marginal Means, Standard Error, Number of Observationsa
At Least One High PS Groups
No High PS Groups
Mean
Std. Er.
Obs.
Mean
Std. Er.
Obs.
Higher Risk
Likelihood of Risk
7.86
0.20
45
7.74
0.18
39
Significance of Risk
8.10
0.20
45
8.00
0.18
39
Pervasiveness of Risk
8.06
0.22
45
7.84
0.19
39
Lower Risk
Likelihood of Risk
7.44
0.20
39
6.87
0.17
48
Significance of Risk
8.05
0.20
39
7.53
0.17
48
Pervasiveness of Risk
7.49
0.22
39
7.30
0.19
48
Panel B: MANCOVA Attributes of Riskb, c
Error
Value
df
F
p
Source
df
School
0.21
6
328
6.29
<0.01
Case Type
0.11
3
163
6.40
<0.01
No High PS Group
0.03
3
163
1.61
0.20
Case Type X No High PS Group
0.02
3
163
0.82
0.48
Panel C: Simple Effect of Case Conditiond
Cont.
Std.
Error
df
F
p
Source
Est.
Er.
df
Likelihood of the Risk
0.65
0.17
1
165
14.23
<0.01
Significance of the Risk
0.26
0.17
1
165
2.35
0.13
Pervasiveness of the Risk
0.56
0.19
1
165
9.05
<0.01
These data represent the average assessed risk of fraud reported by low professional skepticism
participants in their final fraud risk assessments following brainstorming. All fraud risk assessments
were measured using a 10-point Likert scale. The significance of the risk, the pervasiveness of the risk,
and the likelihood of the risk represent attributes of risk as outlined in PCAOB AU Sec. 316, paragraph
40.
a. These data represent the marginal means for low PS participants separated by case type and group
type.
b. This analysis presents a 2X2 full factorial MANCOVA where the outcome variables are the three
attributes of risk and the predictor variables are Group Type and Case Type. School is included in the
analysis as a covariate.
c. The reported test statistics are based on Pillai-Bartlett trace as it tends to be the most conservative.
d. This analysis represents the simple effect of case and the test statistics are based on univariate
ANOVAs. The reference group in this analysis is the low risk case.
37