Effect of fraud risk assessments on auditor skepticism

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O R I G I N A L A R T I C L E

Effect of fraud risk assessments on auditor skepticism:

Unintended consequences on evidence evaluation

Grace Mubako

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Ed O'Donnell

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1College of Business, University of Texas at El Paso, El Paso, TX, USA

2College of Business, Southern Illinois University, Carbondale, IL, USA Correspondence

Grace Mubako, University of Texas at El Paso, College of Business, 500 W University Avenue, El Paso, TX 79968, USA.

Email: gnmubako@utep.edu

This study examines whether auditors who learn that fraud risks differ between accounts could become less skeptical toward evidence that could signal financial misstatement in low‐fraud‐risk accounts. Our theoretical framework suggests that contrast effects could reduce skepticism about suspicious changes in low‐fraud‐risk accounts when auditors perform analytical procedures during the planning phase of assurance engagements. We conducted a laboratory experiment where experienced auditors analyzed year over year changes in accounts to assess misstatement risk for revenue and costs. We manipulated fraud risk for revenue and the presence of an inconsistent fluctuation in costs. Participants who learned that fraud risks had been assessed as high for revenue but low for costs rated misstatement risk at lower levels for cost accounts, compared with auditors who learned that fraud risk was assessed as low for both accounts.

K E Y W O R D S

Analytical procedures, audit evidence, audit judgement, audit methodology, audit planning, fraud

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I N T R O D U C T I O N

Professional standards (ISA 240,The Auditor's Responsibilities Relating to Fraud in an Audit of Financial Statements, and SAS 99,Consideration of Fraud in a Financial Statement Audit) require auditors to learn about conditions that increase the risk of fraudulent financial reporting in order to help them develop a more effective program of audit tests (American Institute of Certified Public Accountants [AICPA], 2002;

International Auditing and Assurance Standards Board [IAASB], 2009). As the risk of fraud increases, professional guidance (ISA 315, Identifying and Assessing the Risks of Material Misstatement through Understanding the Entity and its Environment, and AS 2110,Identifying and Assessing Risks of Material Misstatement) directs auditors to become more skeptical about the potential for financial misstatement (IAASB, 2010; Public Company Accounting Oversight Board [PCAOB], 2010c), and to respond by adjusting allocations of audit effort to planned audit tests (PCAOB, 2010d). In other words, professional standards direct auditors to adjust their skepticism toward evidence that increases the likelihood of misstatement in accounts based on their knowledge about fraud risk for those accounts. Our study examines how knowledge of fraud risk could have the unintended consequence of reducing auditor skepticism.

Research has shown that known fraud risk assessments exert a pow- erful influence on auditor judgment about the risk of misstatement (Blay, Sneathen, & Kizirian, 2007; Trompeter, Carpenter, Desai, Jones, & Riley,

2012). Auditors evaluate evidence more thoroughly (Rose & Rose, 2003) and increase their allocation of audit effort for accounts known to have high fraud risk relative to allocations for accounts known to have a lower risk of fraud (Hammersley, Johnstone, & Kadous, 2011; Mock & Turner, 2005). Auditors may also be reluctant to back off from their skeptical mindset when they allocate audit effort to accounts with lower fraud risk (Kachelmeier, Majors, & Williamson, 2014).

Even when fraud risks do not contrast, auditors can have difficulty recognizing the potential for fraud unless they perform auditing procedures that specifically expose the threat of management manipulation (Hoffman & Zimbelman, 2009), or they acquire knowledge that makes the potential for management manipulation more salient (Bowlin, 2011). Furthermore, managers may exploit auditor vulnerability with regard to awareness of low fraud risk by manipulating accounts where auditors least expect to find misstatements (Bowlin, 2011). If auditors who learn that fraud risks differ among accounts have difficulty recognizing the potential for misstatement in low‐fraud‐risk accounts, and if managers strategically target low‐risk accounts for aggressive financial reporting, then contrast effects from information relating to fraud risk might compromise audit quality by reducing auditor concern about evidence that could signal misstatement.

These equivocal findings from studies that examined how information relating to fraud risk affected auditors' concerns about the likelihood of misstatement shed light on a potentially dangerous gap in our understanding about how fraud risk assessments can influence audit DOI: 10.1111/ijau.12104

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planning decisions. Auditors must integrate their assessments of fraud risk into their assessment of misstatement risk when they evaluate evidence while planning audit engagements (PCAOB, 2010d). However, if awareness of contrasting fraud risks compromises auditor skepticism about evidence that could signal misstatement, then plans for allocat- ing audit effort may not provide an effective response to audit risk.

The research discussed earlier has provided evidence that fraud risk assessments can affect auditors' intentions to allocate audit resources. Our study examined how awareness of contrasting fraud risks influences auditor risk assessments when they acquire evidence that could signal misstatement. We used a laboratory experiment to examine how contrast effects can influence skepticism about inconsistent fluctuations in accounts when auditors perform analytical procedures during the planning phase of an audit engagement.

Experienced auditors evaluated an audit planning case for a company that provides digital data services and assessed misstatement risk by analyzing year‐over‐year changes in accounts.

Our research design included two manipulations that created four experimental conditions. We manipulated information on contrast by reporting either that fraud risk had been assessed as low for both service revenue and service delivery costs (no‐contrast condition), or that fraud risk had been assessed as high for service revenue but low for service delivery costs (contrast condition). We manipulated the presence of evidence that could signal misstatement by reporting a year‐ over‐year change in service delivery costs that was either consistent or inconsistent with changes in client business activities. Differences in participants' misstatement risk assessments for service delivery costs across the four experimental conditions provided a basis for evaluating how contrast effects influenced auditor skepticism toward evidence that could signal misstatement.

The influence of the high fraud risk assessment for service delivery revenue should cause auditor skepticism toward evidence about service delivery cost accounts either to remain unchanged (because, in this business model, service delivery activities function independently from revenue‐generating processes), or to increase (because overall fraud risk increases when fraud risk for revenue increases). However, participants who knew that fraud risk for revenue was higher than fraud risk for costs responded with less skepticism when they encoun- tered evidence that could signal misstatement in costs, compared with participants who knew that fraud risk was the same for revenue and costs. In other words, contrast effects attributable to knowledge of fraud risks had unintended consequences of reducing auditor skepticism toward evidence that could signal misstatement.

The findings from our study are particularly important in the current audit environment where regulators are emphasizing the need for auditors to exercise sufficient professional skepticism (PCAOB, 2012). Auditors are likely to frequently encounter situations where fraud risks contrast across accounts; thus, these findings can help audit practitioners review their procedures, and consider ways to minimize the impact of contrast effects on auditor risk assessments.

In Section 2 we explain our theoretical framework and present research hypotheses that predict our findings. Section 3 describes the experiment that provided data for testing our hypotheses, and Section 4 presents our analysis of results. Finally, in Section 5 we discuss the implications of our findings and acknowledge limitations of the study.

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T H E O R E T I C A L F R A M E W O R K

Auditors develop the program of tests that they will use to gather evidence and substantiate financial statement accounts during the planning phase of assurance engagements (PCAOB, 2010b). They develop procedures for acquiring evidence based on their assessment of the risk that accounts could be misstated (PCAOB, 2010a). To assess misstatement risk, auditors: (i) learn about inherent risks of misstatement attributable to business models, market conditions, and the potential for fraudulent financial reporting; (ii) evaluate the reliability of control procedures clients use to protect accounts from errors and fraud; and (iii) perform analytical procedures to identify suspicious patterns of year‐over‐year changes in accounts that could signal misstatement (PCAOB, 2010c). Perceptions that auditors develop during risk assessment procedures provide a basis for designing a program of audit tests to detect any material misstatements that may have occurred (PCAOB, 2010d).

This study examines how information about fraud risk acquired by auditors when they evaluate inherent risk can influence their judgment about suspicious patterns of changes in accounts they identify during analytical procedures. Messier, Simon, and Smith (2013) explain that, during risk assessment procedures, auditors analyze accounts by using information about client operations to develop expectations about how accounts should have changed from last year to this year, and then searching for fluctuations that do not match their expectations.

When they identify account changes that do not coincide with their expectations, auditors interpret these inconsistent fluctuations in one of two ways. They may conclude that an inconsistent fluctuation represents an insignificant deviation attributable to imprecise expectations about changes in accounts. Alternatively, they may conclude that the inconsistent fluctuation represents a significant anomaly that could signal financial misstatement and should be targeted for addi- tional scrutiny through substantive audit procedures.

In other words, an auditor's tolerance for deviation between expected and observed changes in the accounts they analyze deter- mines their conclusion about the risk of financial misstatement associated with any inconsistent fluctuations they identify. Messier et al.

(2013) suggest that auditors who are more skeptical are more likely to generate fraud explanations in place of nonfraud explanations for account fluctuations. This shows that tolerance for deviations depends, in large part, on the degree of skepticism auditors apply to inconsistencies they identify when they evaluate fluctuations in accounts. (Messier et al., 2013). Thus, as tolerance for deviations decreases, the likelihood that auditors will classify inconsistencies as evidence that could signal financial misstatement increases.

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Auditor skepticism

Auditing standards require auditors to exercise appropriate levels of skepticism in their audit judgments. Auditing literature puts forward several definitions of professional skepticism that reflect a spectrum of the levels of skepticism an auditor can have. AICPA (2002) defines skepticism as“a questioning mind and critical assessment of evidence,” which implies that auditors should not believe or accept evidence without subjecting it to rigorous scrutiny. Nelson (2009) defines

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skepticism as“auditor judgements and decisions that reflect a heightened assessment of the risk that an assertion is incorrect, conditional on the information available to the auditor.”The underlying common theme is that auditors should not take the assertions presented to them at face value, but should obtain and critically assess evidence to support the assertions.

With regard to auditor risk assessments, skepticism reflects the extent to which auditors' perceptions about the risk of misstatement change when they acquire nondeterministic evidence (PCAOB, 2012).

Observing an inconsistent fluctuation during analytical procedures does not provide deterministic evidence that misstatement has occurred. Instead, these inconsistencies between auditor expectations and observed values merely bring into focus factors that auditors should interpret and consider when they assess misstatement risk (PCAOB, 2010a). More skeptical auditors will assess risk at higher levels when they acquire nondeterministic evidence that could signal financial misstatement, compared with less skeptical auditors (Nelson, 2009). As a result, skepticism plays a key role in an auditor's response to the nondeterministic evidence they acquire during analytical procedures.

Nelson (2009) explains that auditors become more skeptical when they learn about conditions that increase the overall potential for misstatement in accounts. He predicts that more skeptical auditors will document greater concern about evidence that could signal financial misstatement, compared with less skeptical auditors who examine the same evidence. In other words, auditors with knowledge of conditions that increase the potential for misstatement apply more skepticism when they evaluate evidence, and as a result they tend to assess misstatement risk at higher levels. Learning about fraud risk provides knowledge that influences auditor skepticism about evidence that could signal misstatement (Payne & Ramsay, 2005; Zimbelman, 1997). Thus, fraud risk assessments can be seen as anchors, or original points of belief that influence interpretation of subsequent evidence.

Prior research in auditing shows that prior beliefs affect subsequent judgments made by auditors (Ashton & Ashton, 1988; Bamber, Ramsay, & Tubbs, 1997; Joyce & Biddle, 1981). When fraud risk is known to be high, skepticism increases because there is a prior belief or expectation of misstatement; and alternatively, when fraud risk is known to be low, there is no prior belief or expectation of misstatement, and therefore skepticism levels are lowered.

Consider the following example about inconsistent fluctuations observed during analytical procedures. Assume that, after learning about client operating activities, auditors expect an expense account to increase by 3–5%. Instead, they observe that the account actually increased by only 1%. Because the actual year‐over‐year change in expenses deviates from the expected change, auditors have identified an inconsistent fluctuation. Auditors must decide whether this unexpected change represents evidence that increases the risk of misstatement for expense accounts, or simply reflects tolerable error in their expectation about how much expenses should have changed from last year to this year.

Auditors who have learned that fraud risk is high should be more likely to interpret this deviation as evidence that signals intentional understatement of expenses, because increased skepticism caused by awareness of elevated fraud risk decreased their tolerance for unexpected fluctuations. In other words, more skeptical auditors should

be less likely to attribute this inconsistent fluctuation to imprecise expectations about changes in expenses. However, auditors who learn that fraud risk is low are likely to have diminished skepticism because of the low expectation of misstatement due to fraud. As a result, these auditors may discount evidence that potentially points to fraud because it conflicts with their prior belief (of no fraud); therefore, they are likely to interpret the deviation as reflective of tolerable error in their expectation about how much expenses should have changed.

As a result, more skeptical auditors should rate the risk of misstatement at higher levels, compared with less skeptical auditors.

To summarize, awareness of information about fraud risk affects auditor skepticism, and skepticism affects auditor tolerance for inconsistent fluctuations during analytical procedures. More skeptical auditors tend to assess misstatement risk at higher levels after observing inconsistent fluctuations because they have a lower tolerance for inconsistent fluctuations than less skeptical auditors. Professional standards assume that auditors' skepticism towards evidence that could signal misstatement increases as fraud risks increase. We suggest that contrast effects could eliminate this positive association between fraud risk and skepticism when auditors observe inconsistent fluctuations while performing analytical procedures.

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Contrast effects

According to Mussweiler (2003), people do not make evaluative decisions in a vacuum, but rather“within and in relation to a specific context.”When these contexts involve contrasting decision information, contrast effects may result. This means that different conclusions about the same evidence can be reached because of the subconscious effect of unrelated attributes of the decision target that differ. Judg- ments become more extreme compared with judgments made in the absence of contrasting information. In other words, exposure to contrasting decision information can distort evaluative judgment about other, unrelated decision cues. For example, auditors who evaluated evidence suggesting that collection risk was high (low) for one portfolio of loans rated risk at lower (higher) levels when they later evaluated evidence for another portfolio of loans that suggested moderate collection risk (O'Reilly, Leitch, & Wedell, 2004). Relatedly, Barr‐Pulliam and Bowlin (2016) found that contrast effects reduced auditors' sensitivity to strategic aspects of the audit. In their experiment, contrast effects changed evaluative judgments about evidence when knowledge that overall risk for the first portfolio was higher, distorted auditors' assessment of risk for the second portfolio by reducing their concern about collectability, even though knowledge about risk for the first portfolio (a contrasting attribute) was irrelevant for evaluating evidence about the second portfolio.

Mussweiler (2003) explains how learning about contrast alters the norms that people use to evaluate decision information. He suggests that people form evaluative judgments by comparing new information to decision norms they subconsciously develop from previously acquired information about a decision target. Mussweiler further explains that preliminary evaluative judgments (perceptions) form quickly based on initial information about a decision target (evidence).

Information that establishes initial perceptions also provides decision norms for processing new evidence. These decision norms specify

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the extent to which new evidence can differ from expectations without altering perceptions (tolerance for deviations). In other words, people develop evaluative judgment by comparing new evidence with expectations based on decision norms, and then determining whether current perceptions should change because new evidence differs from expectations to an extent that exceeds their tolerance for deviations.

Mussweiler (2003) explains that contrast effects can alter evaluative judgment when awareness of differences between attributes dis- torts the decision norms that people use to evaluate new evidence. In other words, the effects of these contrasts can cascade onto subsequent decision tasks by distorting the decision norms people use to evaluate new information. For example, Bhattacharjee, Maletta, and Moreno (2007) asked experienced auditors to assess the reliability of internal audit department for two different clients, and then to evaluate substantive evidence about the inventory account at the second client.

They provided the same evidence about inventory to all participants, but they manipulated risk attributable to reliability of the internal audit department at the first client as either higher or lower than risk for the second client. Auditors who knew that risk attributable to internal audit reliability was higher (lower) at the first client rated misstatement risk for inventory at the second client at lower (higher) levels.

Consistent with Mussweiler's (2003) predictions, Bhattacharjee et al. (2007) found that learning of contrast between risks during the first task altered auditors' skepticism about evidence they evaluated during the second task, even though risks assessed during the first task (evaluating internal audit reliability) had no relevance for assessing risks during the second task (evaluating inventory valuation).

Mussweiler's thesis suggests that contrast effects diminished skepticism in decision norms that auditors used to evaluate evidence about inventory. We suggest that contrast effects from knowledge of fraud risk could diminish auditor skepticism about evidence that could signal financial misstatement.

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Research hypotheses

Mussweiler (2003) explains that the norms people use to evaluate decision information develop from knowledge that is most salient and readily available when the information is acquired. In an audit context, information about fraud risk provides an auditor with salient context for evaluating evidence about accounts (Kachelmeier et al., 2014).

Thus, we consider a scenario where fraud risk affects the auditor's evaluation of evidence. Specifically, we consider how information about high fraud risk for one account versus low fraud risk for another account could influence auditor skepticism about changes in accounts that do not match their expectations (inconsistent fluctuations).

As discussed earlier, auditing standards suggest that skepticism toward inconsistent fluctuations will be positively associated with knowledge of fraud risks (PCAOB, 2010c). However, the contrast theory discussed in our theoretical framework suggests that the positive association between fraud risk and skepticism toward inconsistent fluctuations could actually become negative when auditors know that fraud risks contrast. Thus, when auditors observe an inconsistent fluctuation during analytical procedures, their (salient) knowledge about fraud risk should influence the decision norm they construct to interpret this new evidence.

Assume that auditors know fraud risk for the account they are analyzing is low and not inconsistent with fraud risk for other accounts.

Under these conditions, this information about fraud risk should have little impact on skepticism toward (the norm that is constructed to interpret) the inconsistent fluctuation. However, Mussweiler's thesis also suggests auditors who know that low fraud risk assessments for the account being analyzed contrast with high fraud risk assessments for other accounts will likely overreact to the contrast. Contrast effects should cause auditors to construct decision norms that reflect abnormally high skepticism toward high‐fraud‐risk accounts and abnormally low skepticism toward low‐fraud‐risk accounts. In this case, auditors inappropriately anchor on irrelevant information (fraud risk assessment for unrelated account) and then fail to make sufficient adjustment when it comes to evaluating subsequent evidence. As a result, auditors who know that fraud risks contrast should be less skeptical about evidence that could signal misstatement in low‐fraud‐risk accounts than auditors who know that fraud risks do not contrast. These associations suggest the following research hypothesis:

H1: For low‐fraud‐risk accounts, when auditors encounter inconsistent fluctuation when conducting analytical procedures, they perceive misstatement risk to be lower when fraud risks contrast than when fraud risks do not contrast.

This hypothesis is illustrated diagrammatically in Figure 1, where the downward‐sloping continuous line demonstrates the expected results when auditors find inconsistent fluctuation.

When actual changes in accounts are consistent with auditors' expectations, the association between knowledge of contrasting fraud risk and auditor skepticism should change. Consider how contrast effects from knowledge of fraud risk are likely to differ in the presence versus absence of inconsistent fluctuations. As explained in the ratio- nale for H1, auditors who acquire evidence that is inconsistent with their expectations (inconsistent fluctuations) are likely to integrate knowledge about other related contrasts (contrasting fraud risk assessments) when they construct a decision norm to interpret the evidence.

On the other hand, auditors who acquire evidence that is consistent with their expectations (consistent fluctuations) are likely to ignore knowledge about other related contrasts when they construct a decision norm. In other words, Mussweiler's thesis suggests that awareness of fraud risk contrasts between accounts will influence skepticism when auditors observe fluctuations inconsistent with their expectations, but not when they observe fluctuations consistent with their expectations.

These associations suggest the following research hypothesis:

H2: For low‐fraud‐risk accounts, when auditors encounter consistent fluctuation when conducting analytical procedures, they perceive misstatement risk to be about the same whether or not fraud risks contrast.

This hypothesis is illustrated diagrammatically in Figure 1, where the horizontal dashed line demonstrates the expected results when auditors find consistent fluctuation.

In summary, we hypothesize that contrast effects from information relating to fraud risk will decrease skepticism about the risk of misstatement during analytical procedures when auditors observe

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inconsistent fluctuations, but not when they observe no inconsistent fluctuations. Our hypotheses predict an interaction between contrasting fraud risk and inconsistent fluctuations with respect to auditor perceptions about the risk of misstatement for low‐fraud‐risk accounts depicted graphically in Figure 1.

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M E T H O D

We tested our hypotheses with data collected during a laboratory experiment. Experienced auditors performed analytical procedures to assess the risk of material misstatement in selected accounts. All participants were audit seniors from one Big‐Four firm, with an average of 15.4 months at that rank. They had been trained to assess risk using evidence similar to the information they analyzed during our experiment, and they were responsible for performing similar risk assessments in the field. Participants completed the exercise in the presence of a research proctor while attending national training ses- sions conducted by their employer.

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Procedure

Participants performed analytical procedures to assess account‐level misstatement risks in the context of planning an assurance engagement for a hypothetical audit client that provided video entertainment, inter- net, and telephone service through a cable television network. The

exercise required participants to assess risk of misstatement by analyzing year‐over‐year changes in accounts used to record (i) revenue gen- erated by customer subscriptions and (ii) costs of maintaining the service delivery infrastructure. We created the case based on information filed with the Securities and Exchange Commission by Cox Com- munications. Materials included a description of business processes associated with subscription revenue and service costs, and described procedures that the company used to account for those activities.

Participants began their task by reviewing information about business risk and accounting practices for revenues and costs, and then assessed the overall risk of material misstatement at the financial statement level. Next, they evaluated results from tests of controls and assessed control risk for revenues and costs.¹Next, participants were required to rate the relative importance of the evidence they considered when evaluating the internal controls. Participants were then required to assess the (pre‐task) risk of material misstatement for the revenue and cost accounts. Next, participants provided demographic information before moving on to the main task, the analytical procedure. Participants learned about fraud risk assessments for revenue and cost accounts (that had been developed earlier by other members of the audit team), performed analytical procedures on year‐over‐year changes in financial statement accounts and key performance indicators (this year, last year, amount of change, and per- centage change), and documented their (post‐task) assessments of misstatement risk for revenue and cost accounts. Figure 2 is a flow- chart showing the experimental procedures.

FIGURE 1 Predicted influence of contrast effects and inconsistent fluctuations on auditor perceptions about the risk of misstatement

(1) Financial statements provided in case materials included fluctuation in costs that is inconsistent with expectations based on other operating information

(2) Financial statements provided in case materials included fluctuation in costs that is consistent with expectations based on other operating information

(3) Fraud risk for both revenue and cost accounts is low

(4) Fraud risk for revenue account is moderately high, fraud risk for costs account is moderately low

Inconsistent fluctuation⁽¹⁾ Consistent fluctuation⁽²⁾

No⁽³⁾ Yes⁽⁴⁾

Contrast between fraud risks High

Perceived misstatement risk

Low

Review information about business risks, accounting practices for revenues and costs and engagement risk based on continuation review

Assess risk of material misstatement at financial statement level

Review information on internal controls of revenues and

Assess control risk for revenues and costs

Rate relative importance of information used to evaluate control risk Assess the risk of material misstatement for the revenue and cost accounts

Provide demographic information

Review financial, fraud risk, and operating information and perform analytical procedures Assess risk of

misstatement (and other relevant risks) for revenues and costs

FIGURE 2 Flow chart of experimental procedures

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Variables

We manipulated two experimental conditions with regard to analytical procedures, including presence versus absence of (i) contrasting fraud risk assessments and (ii) evidence that could signal misstatement.

Participants were randomly assigned to one of four experimental conditions resulting in a 2 × 2 between‐subjects design. Materials provided to participants assigned to the contrast condition reported that fraud risk for revenue and cost accounts had previously been assessed as moderately high and moderately low respectively. Partici- pants in the no‐contrast condition received materials that indicated fraud risk for both revenue and cost accounts had been assessed as low. Case materials for the seeded inconsistency condition included an inconsistent fluctuation in service delivery costs, but materials for the no inconsistency condition did not.

We created a seeded inconsistency by manipulating the year‐ over‐year change in service delivery costs. All participants were told that (i) hourly labor costs for service delivery had increased slightly while all other service costs had remained relatively unchanged from the prior year, and (ii) infrastructure repairs and system maintenance demands had been about the same as last year. These conditions suggest that service delivery costs should have increased slightly from last year to this year. Materials provided to participants in the no inconsistency condition reported that costs had increased by 2.5%. Materials for the seeded inconsistency condition reported that costs had decreased by 2.5%. This pattern of evidence should create an unexpected fluctuation in the seeded inconsistency condition because a decrease in service delivery costs contradicts information that labor costs increased and maintenance demands were about the same as last year.

The dependent variable we used to test our hypotheses was the pre‐to‐post‐task change in misstatement risk assessments for costs.

This variable was calculated as the difference between the pre‐task misstatement risk assessment and the post‐task misstatement risk assessment. Eliciting pre‐task risk assessments after participants acquired evidence about business and control risk, but before they learned about fraud risk and acquired evidence from analytical procedures, allowed us to measure the extent to which our experimental manipulations altered participants' perceptions of misstatement risk (by calculating pre‐to‐post‐task change).

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D A T A A N A L Y S I S

The 93 auditors who participated in our experiment had an average of 37.8 months of audit experience (standard deviation [SD] 9.8 months),

and had held the rank of senior for an average of 15.3 months (SD 7.3 months). Of the 44 participants assigned to the contrast condition, 23 received case materials that included the seeded inconsistency and 21 received materials with no inconsistencies. Of the 49 participants assigned to the no‐contrast condition, 23 received case materials that included the seeded inconsistency and 26 received case materials with no inconsistencies.² We present descriptive statistics for measured variables in Tables 1 and 2.

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Manipulation checks

After performing analytical procedures and documenting assessments of misstatement risk for revenue and cost accounts, participants reported their perceptions about fraud risk for revenue and cost trans- actions on an integer scale, where 1 corresponded to low risk and 5 corresponded to high risk. Consistent with our intentions, participants assigned to the no‐contrast condition rated the risk of fraud for revenue accounts at an average of 2.7, compared with an average of 3.4 for participants assigned to the contrast condition (t= 3.45;

p= . 0009). We also use a within‐subjects test to check our contrast manipulation. We calculate a difference score for each participant by subtracting fraud risk ratings for costs from fraud risk ratings for revenue. Difference scores in the no‐contrast condition averaged 0.46, compared with 1.40 in the contrast condition (t= 4.35;p< . 0001).

We conclude that our contrast manipulation was successful.

If our seeded‐inconsistency manipulation was successful, then risk assessments for cost accounts should be higher (lower) in the presence (absence) of the seeded inconsistency. Because we expect contrast effects to alter misstatement risk assessments for cost accounts, we check our manipulation with data from the no‐contrast experimental condition. The average change in pre‐task to post‐task ratings of misstatement risk for costs is−0.9 in the no‐inconsistencies/no‐contrast condition, as compared with– 0.2 in the seeded‐inconsistency/no‐ contrast condition (t= 2.21;p< . 05). Because changes in risk ratings for participants assigned to the no‐contrast condition suggest significantly higher levels of concern about the potential for misstatement in cost accounts when the seeded inconsistency was present (evi- denced by a smaller decrease in pre‐to‐post‐task risk assessments), we conclude that our seeded inconsistency manipulation was successful.

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Hypothesis tests

We test both of our research hypotheses by using analysis of covariance to examine how the experimental manipulations influenced pre‐

TABLE 1 Descriptive statistics: Measured variables

Value

Variable

Mean

(n= 93) SD Minimum Maximum

Months at the senior rank 15.3 7.3 8.0 48.0

Average pre‐task control risk assessments^a 3.2 0.7 2.0 5.0

Pre‐to‐post‐task change in risk assessments for service delivery costs −0.6 1.1 −4.0 3.0

Risk of fraud in revenue^a 3.1 1.0 1.0 5.0

aFraud risk for both revenues and costs accounts is low.

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to‐post‐task changes in misstatement risk assessments for cost accounts, while controlling for the influence of differences in participants' perceptions about control risk attributable to evidence that we manipulated for another study.³Tables 3 and 4 present results from the analysis of covariance.

Our research hypotheses predict that contrast effects (from knowledge of fraud risks) will (will not) alter concern about risk of

misstatement (skepticism) when auditors encounter (do not encounter) inconsistent fluctuations (evidence that could signal misstatement).

The significant interaction between contrasting fraud risk and seeded inconsistency (f= 14.6;p= . 0002) reported in Table 3 provides evidence that, in part, supports our predictions. Figure 3 illustrates this interaction by providing a graphical representation of findings presented in Table 4.

Our first hypothesis predicts that auditors who encounter inconsistent fluctuations will assess misstatement risk at lower levels when they know that fraud risks contrast. As reported in Table 4, average pre‐to‐post‐task change in misstatement risk ratings for the seeded inconsistency condition decreased significantly more (t= 2.86;

p< . 01) for participants in the contrast condition (−1.1), compared with participants in the no‐contrast condition (−0.2). These results provide evidence that participants were less skeptical with regard to misstatement risk for low‐fraud‐risk accounts when they observed unexpected fluctuations after learning that fraud risks contrasted. is supported.

Our second hypothesis predicts that the misstatement risk assessments of auditors who do not encounter inconsistent fluctuations will notdiffer, whether or not fraud risks contrast. As reported in Table 4, average pre‐to‐post‐task change in misstatement risk ratings for the no‐inconsistency condition did not decrease more for participants in the contrast condition, compared with participants in the no‐contrast condition. Instead, evidence suggests that participants who observed no inconsistencies were actually more concerned about misstatement TABLE 2 Descriptive statistics: Risk assessments for service delivery costs by experimental condition

Risk assessment

No contrast between fraud risk^a Contrast between fraud risk^b

No inconsistency^c Inconsistency^d No inconsistency^c Inconsistency^d

Mean SD Mean SD Mean SD Mean SD

Pre‐task^e 3.5 1.1 2.8 1.1 2.2 0.7 3.5 0.1

Post‐task^e 2.5 1.1 2.5 0.9 2.1 0.9 2.2 1.0

Change^f −0.9 0.9 −0.2 1.2 −0.1 0.7 −1.1 1.3

aFraud risk for both revenues and costs accounts is low.

bFraud risk for revenues is moderately high, fraud risk for costs is moderately low.

cFinancial statements provided in case materials included fluctuation in costs that is consistent with expectations based on other operating information.

dFinancial statements provided in case materials included fluctuation in costs that is inconsistent with expectations based on other operating information.

eElicited on an integer scale from 1 (low) to 5 (high).

fChange is calculated as the difference between the pre‐task and post‐task risk assessments.

TABLE 3 Influence of experimental manipulations on auditor skepticism:^aanalysis of covariance

Degrees of freedom

Sum of squares

f‐

statistic p‐

value

Model 4 29.4 6.3 .0002

Error 88 102.8

Experimental manipulations:

Contrasting fraud risks 1 0.1 0.1 .8096

Seeded inconsistency 1 0.5 0.4 .4977

Interaction 1 17.0 14.6 .0002

Covariate:

Pre‐task control risk assessments^b

1 7.2 6.2 .0146

aDependent variable: pre‐to‐post‐task change in misstatement risk assessments for service delivery costs elicited on an integer scale from 1 (low) to 5 (high).

bRisk that controls would fail to prevent material misstatements elicited before the experimental task began. Assessments were reported on an integer scale from 1 (low) to 9 (high) based on results from tests of controls.

TABLE 4 Influence of experimental manipulations on auditor skepticism: Adjusted cell means

No inconsistency^a Inconsistency^b Condition mean t

No contrast between fraud risks^c −0.9n= 26 −0.2n= 23 −0.6n= 49 2.30**

Contrast between fraud risks^d −0.1n= 21 −1.1n= 23 −0.6n= 44 3.06***

Condition mean −0.5n= 47 −0.7n= 46 0.68

t 2.56*** 2.86*** 0.24

**p< . 05;

***p< . 01.

aFinancial statements provided in case materials included fluctuation in costs that is consistent with expectations based on other operating information.

bFinancial statements provided in case materials included fluctuation in costs that is inconsistent with expectations based on other operating information.

cFraud risk for both revenues and costs accounts is low.

dFraud risk for revenues is moderately high, fraud risk for costs is moderately low.

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risk when they knew that fraud risks contrasted, compared with participants who knew that fraud risks did not contrast. The risk assessments decreased significantly more (t= 2.56;p< 0.01) for the participants in the no‐contrast condition (0.9), compared with those in the contrast condition (−0.1). Thus, while, as expected, changes in risk assessments do not decrease more in the contrast condition, H2 is not quite supported, in that the change in risk assessments actually decreased significantly less than in the no‐contrast condition.

We believe it likely that participants assigned to the no‐inconsistencies condition were more concerned about misstatement risk when fraud risks contrasted because of the manipulation we used to create contrast. Participants in the contrast condition were told that fraud risk was high for service revenue but low for service delivery costs. In the no‐contrast condition, participants were told that fraud risk was low for both revenue and cost. As a result, participants who knew that fraud risk was high for revenue should have been more concerned about overall fraud risk than participants who knew that fraud risk was low for revenue. In other words, participants in the contrast condition should have developed greater concern about the overall risk of misstatement when they learned about fraud risks. Greater concern about misstatement at the beginning of analytical procedures would carry over to manifest in greater concern about misstatement at the end of the task, which likely increased participants' ratings of misstatement risk for cost accounts.

We did not elicit perceptions about overall fraud risk during our experiment. However, for participants assigned to the no‐inconsistencies‐with‐contrasting‐fraud‐risk condition, we find a positive Pear- son correlation of 0.34 (p= . 0186) between their perceptions of fraud risk for revenue and the pre‐to‐post‐task change in misstatement risk for costs. In other words, consistent with the explanation offered earlier, misstatement risk assessments for cost accounts increased as fraud risk assessments for revenue accounts increased. It appears that, when there was no inconsistent fluctuation to trigger contrast effects, participants' skepticism about misstatement risk was not deflated by exposure to information about contrasting fraud risk assessments.

Instead, participants' integrated knowledge of high fraud risk for revenue into their assessments of misstatement risk for other accounts;

that is, their concern about misstatement risk for costs increased along with their overall concern about fraud risks.

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D I S C U S S I O N

This study examined whether knowledge that fraud risk differs between accounts can reduce auditor skepticism during analytical procedures. In a laboratory experiment, experienced auditors performed analytical procedures to assess the risk of misstatement for two unrelated accounts. One group learned that fraud risk was low for both accounts, while another group learned that fraud risk was high for one account but low for the other. Auditors who knew that fraud risk contrasted between accounts rated misstatement risk for the low‐fraud‐risk account at lower levels when they acquired evidence that could signal misstatement, compared with auditors who knew that fraud risks did not contrast. However, these contrast effects did not manifest when auditors did not encounter inconsistent evidence. Instead, participants were even more concerned about possible misstatement in the low‐fraud‐risk account where fraud risk contrasted.

These findings suggest that contrast effects resulting from considering information about fraud risks can reduce auditor skepticism toward evidence during risk assessment procedures. Such contrast effects could potentially be responsible for the findings of Bowlin (2011), who demonstrated that knowledge of contrasting fraud risks could influence audit strategy by reducing concern about the veracity of accounts. Auditors commonly plan engagements after learning that fraud risks differ across accounts. When considered in conjunction with evidence reported by Bowlin (2011), findings from this study bring to light the need for audit regulators and practitioners to develop a more thorough understanding of how exposure to information about fraud risk affects auditor skepticism. If knowledge of contrast between other types of audit risk influences auditor judgment in the field, like knowledge of contrast between fraud risks influenced auditor skepticism during our experiment, then contrast effects could pose a significant threat to audit quality. This is particularly concerning considering that some accounts, like those for revenue, are inherently more prone to misstatement due to fraud than others. As suggested by Bowlin (2011), unscrupulous managers could take advantage of this audit weakness by strategically targeting those low‐fraud‐risk accounts for more aggressive financial reporting.

Results of H2 suggest that the influence of contrasting fraud risk assessments may not persist in the absence of inconsistent fluctuations.

(1) Financial statements provided in case materials included fluctuation in costs that is inconsistent with expectations based on other operating information

(2) Financial statements provided in case materials included fluctuation in costs that is consistent with expectations based on other operating information

(3) Fraud risk for both revenue and cost accounts is low

(4) Fraud risk for revenue account is moderately high, fraud risk for costs account is moderately low

No⁽³⁾ Yes⁽⁴⁾

Contrast between fraud risks

Inconsistent fluctuation⁽¹⁾ Consistent fluctuation⁽²⁾ High

Perceived misstatement risk

Low

FIGURE 3 Influence of experimental manipulations on auditor perceptions about the risk of misstatement

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This is somewhat consistent with Kachelmeier et al. (2014), who found that, when considering “intentional” risks (as that of fraud), auditors appeared reluctant to back off their skeptical mindset, even when faced with low‐fraud‐risk assessments. Instead, the skeptical mindset developed when the auditor learns about high fraud risks in at least one of the accounts being evaluated is carried over to the overall misstatement risk assessments. Thus, it appears that this carryover effect dominates contrast effects in the absence of inconsistent fluctuations.

Whereas our study examines the potential impact of contrast effects on low‐fraud‐risk accounts, our theoretical framework suggests that auditors could also respond withmoreskepticism to evidence that suggests misstatement if they have been prompted by contrast effects.

Prior research suggests that, when auditors are prompted, skepticism attributable to knowledge of fraud risk increases their overall skepticism; for example, see Hoffman and Zimbelman (2009), Hammersley, Bamber, and Carpenter (2010), and Bowlin (2011). Thus, auditors who encounter contrast conditions as those examined in this study could become overly skeptical about inconsistent fluctuations in high‐fraud‐risk accounts when they are contrasted with low‐fraud‐risk accounts. If contrast effects can also increase auditor skepticism, that could enhance audit quality, but could also compromise the efficient allocation of audit effort if misstatement risk was overestimated.

A limitation of our study is that it did not take into consideration individual levels of inherent skepticism that can affect audit judgment.

Previous research has suggested that skepticism can also be considered an individual trait that an auditor inherently possesses (Hurtt, 2010). Thus, it is likely that an auditor with inherently high levels of skepticism may be less prone to contrast effects as they would be unwilling to tolerate even the lowest level of inconsistency without question, but a less inherently skeptical auditor would.

To summarize, results from this study demonstrate that knowledge of fraud risk can have unintended consequences with regard to auditor skepticism toward evidence that could signal financial misstatement.

Under some conditions, contrast effects attributable to knowledge of fraud risk may inappropriately diminish auditor skepticism toward evidence about accounts. The theoretical framework we used to predict and explain the influence of contrast effects provides a basis for examin- ing how knowledge of contrast could threaten the effectiveness of auditor risk assessments in other judgment contexts. Findings from this study demonstrate how potentially irrelevant information related to an unrelated account can introduce biases that affect an auditor's skepticism.

This highlights the need for auditors to put in place processes and procedures that will help de‐bias any attitudes and perceptions that may impact their skepticism and, in turn, their audit judgments. Research that furthers our understanding of associations between knowledge of inherent risks and interpretation of audit evidence could make substantial con- tributions toward improving audit quality.

E N D N O T E S

1Control risk assessments were elicited for use in another study. To ensure that this manipulation of evidence about control effectiveness did not interfere with results for this study, it was counterbalanced across the experimental conditions we created for this study. We found no evidence that participants' control risk assessments interacted with our manipulation of contrast effects and seeded inconsistency to influence the dependent variable used for this study. We however included participants'

control risk assessments as a covariate in the analysis of variance we used to test our hypotheses to control for any potential influence of the control manipulations on participant misstatement risk assessments.

2The numbers of participants assigned to each experimental condition are not equal because the experiment was administered to multiple groups of participants. Also, the total number of participants does not allow for a balanced distribution of participants among experimental conditions.

3In this study we test for the influence of fraud risk only; therefore, it is necessary to control for any differences in control risk that would usually be a factor in assessing risk of misstatement.

O R C I D

Grace Mubako http://orcid.org/0000-0001-6028-3047 Ed O'Donnell http://orcid.org/0000-0002-9605-987X

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Grace Mubakois an assistant professor at University of Texas at El Paso. She conducts research in auditor judgment and decision‐ making with emphasis in professional skepticism and fraud.

Ed O'Donnellis the Emerson Groennert Professor of Accountancy at Southern Illinois University Carbondale. His applied research examines ways to improve auditing decisions and develop more effective frameworks for enterprise risk management and information technology governance.

How to cite this article: Mubako G, O'Donnell E. Effect of fraud risk assessments on auditor skepticism: Unintended consequences on evidence evaluation. Int J Audit. 2017;1–10.

https://doi.org/10.1111/ijau.12104