Managing audits to manage earnings: The impact of diversions on an auditor's detection of earnings...

Accounting, Organizations and Society xxx (2014) xxx–xxx

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Accounting, Organizations and Society

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Managing audits to manage earnings: The impact of diversionson an auditor’s detection of earnings management

http://dx.doi.org/10.1016/j.aos.2014.07.0050361-3682/� 2014 Elsevier Ltd. All rights reserved.

⇑ Corresponding author. Address: Babson College, Accounting & LawDivision, Babson Park, MA 02457, United States. Tel.: +1 (781) 239 5995;fax: +1 (781) 239 5930.

E-mail addresses: [email protected] (B.L. Luippold), [email protected] (T. Kida), [email protected] (M.D. Piercey),[email protected] (J.F. Smith).

Please cite this article in press as: Luippold, B. L., et al. Managing audits to manage earnings: The impact of diversions on an adetection of earnings management. Accounting, Organizations and Society (2014), http://dx.doi.org/10.1016/j.aos.2014.07.005

Benjamin L. Luippold a,⇑, Thomas Kida b, M. David Piercey b, James F. Smith b

a Babson College, United Statesb University of Massachusetts Amherst, Isenberg School of Management, United States

a b s t r a c t

This study examines an aspect of earnings management that we refer to as audit manage-ment. We define audit management as a client’s strategic use of diversions to decrease thelikelihood that auditors will discover earnings management during the audit. Specifically,we examine whether diverting auditors’ attention to either clean financial statementaccounts or accounts that contain other errors affect an auditor’s ability to uncover earn-ings management. Auditors performed analytical review, searching financial statementsfor unusual fluctuations suggestive of errors. Following prior studies, we seeded an inten-tional accounting error which created an unusual fluctuation that allowed the client tomeet an earnings target. We manipulated whether management provided a diversionarystatement that explicitly identified risk in other areas of the audit, and whether those areaswere clean or contained other detected errors that had no impact on earnings. We find thatauditors’ earnings management detection is worst when they are diverted to cleanaccounts and best when auditors are diverted to accounts that contain other errors. Ourresults suggest that managers can potentially exploit an audit management tactic as simpleas a diversion to a clean area to reduce auditors’ effectiveness at detecting earnings man-agement. The implications of these findings for audit and decision making research arediscussed.

� 2014 Elsevier Ltd. All rights reserved.

Introduction

Earnings management has been a topic of great interestin both the popular press and academic literature (e.g.,Chen, Kelly, & Salterio, 2011; Dechow, Hutton, Kim, &Sloan, 2012; Guerrera, 2012). In fact, attempts to manipu-late financial performance have become so widespreadthat books have been written on earnings managementstrategies (e.g., Giroux, 2003; McKee, 2005). This study dis-

cusses an aspect of earnings management that we refer toas audit management. We define audit management as aclient’s strategic use of diversions to decrease the likeli-hood of auditors discovering managed earnings duringthe audit. Evidence suggests that managers strategicallyattempt to conceal earnings management (e.g., Beasley,Carcello, Hermanson, & Neal, 2010; Bowlin, Hobson, &Piercey, 2014; Knapp, 2010).

Our study investigates whether managers who manipu-late earnings can successfully employ diversions to influ-ence auditors’ detection of unusual fluctuations duringanalytical review. That is, we investigate whether diversion-ary statements made by the client (i.e., identifying areas ofrisk in the financial statements to lure the auditor away frommanaged earnings) affect an auditor’s detection of managedearnings contained elsewhere in the financial statements. In

uditor’s

http://dx.doi.org/10.1016/j.aos.2014.07.005

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2 B.L. Luippold et al. / Accounting, Organizations and Society xxx (2014) xxx–xxx

an experiment, we seeded an earnings management error(i.e., an intentional misstatement used to meet an earningstarget) into financial statements, creating an unusual fluctu-ation for auditors to detect.1 We test whether auditors detectearnings management when they are diverted to: (1)accounts that contain no errors, or (2) accounts that do con-tain errors, but have no impact on earnings.

In the study, auditors completed analytical review pro-cedures on the financial statements of a hypothetical clientin order to determine if any unusual fluctuations (sugges-tive of errors) were present. In all conditions, an earningsmanagement error (which reduced compensation expenseand accruals) was embedded into the financial statements,creating an unusual fluctuation in that account thatresulted in the client meeting analysts’ forecasted earn-ings. We manipulated whether or not management pro-vided a diversionary statement that informed the auditorof a personnel change in the department responsible fornon-current assets. This statement was designed to elevatethe perceived misstatement risk in that area and lure theauditor away from the earnings manipulation. We alsomanipulated whether the accounts in this other area iden-tified by the diversionary statement were clean or con-tained other errors that offset, and therefore had noimpact on earnings.

Managers may be motivated to divert auditors to areasthat contain, or do not contain, other errors. For example, ifmanagers point auditors to ostensibly risky areas that areclean, auditors may conclude that the client’s accountsare likely to be accurate in other areas as well. This wouldmost likely result in a strong diversion effect. Conversely,management may want to direct auditors to areas thatcontain other errors, thinking that these other errors mayoccupy their attention, leading auditors to feel satisfiedthat they are detecting misstatements and ‘‘doing theirjob,’’ resulting in auditors feeling less compelled to dis-cover other errors. However, auditors are also trained topractice professional skepticism (Nelson, 2009;Quadackers, Groot, & Wright, 2014), and the diversion tothe other errors should elevate their sensitivity to the riskof material misstatement in the remainder of the financialstatements, resulting in greater overall audit effort and agreater likelihood that they would find the earningsmanipulation. We therefore investigate the impact of man-agement intentionally directing auditors to both cleanaccounts and accounts containing errors.

Our findings are consistent with our predictions. Specif-ically, we find that auditors’ detection of earnings manage-ment was worst when they were diverted to clean financialstatement accounts, and best when they were diverted toaccounts containing other errors, with earnings manage-ment detection in between these levels when no diversionswere used (whether other errors were present or not).Overall, these results suggest that if management directsauditors to accounts that contain errors, the discovery ofthose errors heightens their sensitivity to errors in other

1 While auditing standards typically characterize unintentional mis-statements as errors and intentional misstatements as fraud, we use theterm error in its more generic sense to refer to any departure fromaccuracy.

Please cite this article in press as: Luippold, B. L., et al. Managing audidetection of earnings management. Accounting, Organizations and Societ

areas of the audit. However, if auditors are directed toclean accounts, the use of diversionary statements candeter auditors from finding earnings management. Thesefindings have significant implications for auditors anddecision researchers in general.

Testing diversions to accounts both with and withoutother errors allows us to demonstrate significantly differ-ent reactions from auditors to two basic strategies thatmanagement could use to divert auditors from an areaused to manage earnings. The different effects also providepossible insight into why diversions to clean areas may bean effective means of concealing earnings management. Onone hand, even though the other errors do not impact earn-ings, diverting auditors toward them increases auditors’sensitivity to the risk of material misstatement in otherareas of the audit. In contrast, diverting auditors to osten-sibly risky areas that turn out to be clean appears to makethose auditors less vigilant in their search for errors else-where in the financial statements. These results suggestthat managers can potentially exploit an audit manage-ment tactic as simple as a diversion to a clean area becausesuch a diversion reduces auditors’ effectiveness at detect-ing earnings management elsewhere in the financial state-ments. This extends the literature on auditor skepticism byidentifying one type of claim that managers could make tomislead auditors without raising red flags (Nelson, 2009;Quadackers et al., 2014). Additionally, it contributes tothe accounting literature on auditing and earnings man-agement with evidence of how managers can concealmaterial misstatements from auditors in order to manageearnings (Beasley et al., 2010; Beasley, Carcello,Hermanson, & Lapides, 2000; Boone, Khurana, & Raman,2012; Caramanis & Lennox, 2008; Chen et al., 2011).

Our study also contributes to decision making research(in both psychology and auditing) that investigates theeffectiveness of diversionary tactics. As we explain in Sec-tion ‘Background literature’, psychology literature on dis-traction suggests that diversions to other errors would bean effective means of concealing earnings management.In contrast, our finding that earnings management detec-tion is greatest when auditors are diverted to accounts thatcontain errors suggests that our context provides a bound-ary condition to the predictions of the psychology litera-ture on distractions, based on the task-specificexperience of auditors and their reaction to the othererrors. Furthermore, our findings contribute to theaccounting and psychology literature on information pur-suit effects (Bastardi & Shafir, 1998, 2000; Nelson &Tayler, 2007; Redelmeier, Shafir, & Aujla, 2001), by demon-strating how management diverting auditors to searchother accounts can amplify auditors’ reactions to what is(or to what is not) in those other accounts. Thus, we notonly contribute to the accounting literature on earningsmanagement and auditing, we also contribute to the gen-eral judgment and decision making literature on diversion,distraction, and information pursuit effects.

Diversions can have important practical implicationsbeyond the setting of deliberate earnings management.For example, even when managers are not deliberatelymanaging earnings with a particular account, they maystill prefer that auditors pay more attention to areas where

ts to manage earnings: The impact of diversions on an auditor’sy (2014), http://dx.doi.org/10.1016/j.aos.2014.07.005


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they are less likely to find problems. As a result, they maydivert auditors from higher risk areas to accounts that theybelieve are not at risk of misstatement. Our findings sug-gest that auditors would be susceptible to these diversionsas well. However, to the extent that errors can occur any-where, managers might mistakenly direct auditors’ atten-tion to an area with errors, which could backfire onmanagers. Even more broadly, managers may inadver-tently direct auditors’ attention from an account thatmaterially misstates earnings to another account that doesnot. No matter the cause of the diversion, our results dem-onstrate that such diversions significantly influence anauditor’s detection of a material misstatement of earningselsewhere in the financial statements.

In the next section, we introduce background literaturerelated to both earnings and audit management, and dis-cuss relevant theories related to our research. After thebackground literature, we describe the method, while thefinal two sections present the results and concludingremarks, respectively.

Background literature

Earnings management

Earnings management refers to financial reportingpractices designed to achieve desired or favorable financialresults (e.g., smoothing earnings, meeting earnings targets)(Bouillon, 2007; Jackson & Pitman, 2001; McKee, 2005;Millstein, 2005). Management faces several pressures, suchas meeting analysts’ forecasts, which may prompt them toresort to such practices (Duncan, 2001). Evidence suggeststhat these short-term pressures can take priority overlong-term economic growth, as research has found thatexecutives sometimes sacrifice economic value to smoothearnings or hit an earnings target (Bhojraj & Libby, 2005;Graham, Harvey, & Rajgopal, 2005).

Archival research provides substantial evidence thatearnings management occurs (e.g., Dechow et al., 2012).For example, several studies have examined specificaccrual accounts that clients use to manage earnings(e.g., Dhaliwal, Gleason, & Mills, 2004; Marquardt &Wiedman, 2004). More striking, however, is evidence sug-gesting that managers sometimes resort to fraudulentmeasures to manage earnings (e.g., Beasley, Carcello, &Hermanson, 1999; Beasley et al., 2000, 2010; Farber,2005; Jones, Krishnan, & Melendrez, 2008).2 However, inorder for management to successfully report over-aggressiveor fraudulent earnings, auditors must fail to discover howand where income is being manipulated. Yet, how managerscan successfully divert auditors’ attention from earningsmanipulations remains an important unanswered questionfor researchers (Bell, Peecher, & Solomon, 2005; Peecher,Schwartz, & Solomon, 2007).

2 Dyck, Morse, and Zingales (2010) estimate archivally that at least onefinancial reporting fraud is ongoing at any time in at least 11.2–13.2% ofpublic companies with more than $750 million in assets, and that managerssuccessfully conceal a large majority of these frauds for some time fromauditors, SEC enforcement, and other governance mechanisms.


Audit management

We define audit management as a client’s strategic useof techniques to reduce the likelihood that auditors willidentify or recognize managed earnings during the audit.Managing the audit may include a variety of methods.For instance, managers may frame evidence in certainways to manipulate the level of perceived risk (e.g.,Jamal, Johnson, & Berryman, 1995; Johnson, Grazioli, &Jamal, 1993). They may provide the auditor with incom-plete or incorrect information to cover-up questionableaccounting practices, or they may use diversions to preventthe auditor from uncovering earnings management, whichis the primary focus of this study.

Diversions are methods designed to direct an auditor’sattention away from a certain audit area. For example,management may use diversionary statements whichidentify specific areas of risk in other areas of the financialstatements in an effort to lure the auditor away from theaccounts used to manage earnings. In this study, we inves-tigate the impact of diversions to areas that do not containerrors, or to areas where errors exist, but have no impacton earnings.

Managers could identify a clean area of the financialstatements as high risk, hoping that auditors would thenconclude that the rest of the financial statements are likelyto be error-free as well. On the other hand, managers coulddirect auditors to areas containing errors for several rea-sons. The other errors may occupy auditors’ attention,and more attention paid to one area of the audit may resultin less attention paid to other areas. In addition, auditorsmay feel satisfied that they have found errors, makingthem less likely to search for earnings management else-where in the financial statements. Finally, managementmay feel that pointing out areas that lead to error discov-ery may increase the trust that auditors have in them,resulting in auditors performing less work in areas thatmanagement suggests are problem free.3 However, diver-sions to other errors may also have the opposite effect. Ifmanagement diverts an auditor to another account by iden-tifying it as high risk, and yet apparently did nothing toremove errors there, the diversion to those errors wouldsend an especially negative signal about management’sinternal controls, which could lead auditors to search moreextensively for errors in other areas of the audit. As a result,we investigate auditors’ detection of earnings managementwhen they are diverted to areas that contain and do not con-tain errors.

While clients may attempt to ‘‘manage’’ many differentaspects of the audit, an area that is of particular interest isanalytical review. Analytical review is used to determinethe extent of required detailed testing in different auditareas, and sometimes is the only audit procedure used totest certain accrual based accounts (AICPA, 2012;

3 Our conversations with practitioners suggest that this and similartactics occur in practice. For example, a former manager of a technologycompany indicated that, when auditors found error corrections that wouldreduce earnings, he would direct them toward other error corrections thatwould increase earnings. Similarly, an audit partner indicated that man-agers may indeed see the audit as a diversionary game.




Ricchiute, 2006) and income statement accounts (FRC,2011a). For example, unless specific risks are identifiedthat warrant detailed testing, compensation accruals aretypically audited using only some variant of analyticalreview (Ricchiute, 2006).4 Thus, if such an account containserrors that go undetected during analytical review, theremay not be subsequent procedures in the audit plan todetect them. Failure to highlight the highest-risk accountsduring analytical review impacts planning, risk assessmentand substantive testing throughout the remainder of theaudit (FRC, 2011a; Messier, Simon, & Smith, 2013; PCAOB,2010b).

Regulators indicate that analytical review is a recurringproblem in their inspections of audit firms (e.g., FRC,2011a, 2011b, 2012b; IFIAR, 2012; PCAOB, 2008b).5 Theseregulatory findings include auditors’ tendency to performanalytical procedures in insufficient depth, as well as theirtendency to rely on and accept managers’ claims during ana-lytical review without corroboration (e.g., FRC, 2011a,2011b, 2012a; PCAOB, 2008a). Trompeter and Wright(2010) report that auditors are increasingly using analyticalreview to reduce the amount of detailed audit tests, usingless experienced auditors to perform analytical review, andincreasingly relying on their clients’ uncorroborated claimsfor guidance during analytical review. These problems sug-gest that auditors could be vulnerable to tactics used bymanagers during analytical review to divert them away frommanaged earnings, especially if done on the pretext ofdirecting them to other risky areas. If successful, such tacticscould lead auditors to significantly reduce or even eliminatefurther detailed tests of the accounts from which they werediverted (FRC, 2011a). Thus, consistent with prior research,we focus on analytical review, since failure to detect mis-statements during analytical review can influence theplanned detailed tests and trajectory of the rest of the audit(e.g., Asare, Trompeter, & Wright, 2000; Asare & Wright,2003; Brewster, 2011; Ismail & Trotman, 1995; Knapp &Knapp, 2001; Knechel, Salterio, & Kochetova-Kozloski,2010; Luippold & Kida, 2012; Messier et al., 2013; Moreno,Bhattacharjee, & Brandon, 2007; Peecher, Piercey, Rich, &Tubbs, 2010; Trompeter & Wright, 2010; Trotman &Wright, 2012; Yip-Ow & Tan, 2000).

Given that managers may attempt to manage the audit,the question therefore arises, can managers employ diver-sionary tactics that allow them to effectively manage earn-ings? We first review relevant theories and research todevelop our predictions. On one hand, prior psychologicalresearch on distraction suggests that diversions to bothclean accounts and to accounts containing other errorsshould be effective. On the other hand, audit practice andresearch suggests that auditors display professional skepti-cism and are likely to react to discovering errors (even

4 In fact, when pretesting this study’s experimental materials, a Big Fouraudit manager commented on how an error in compensation wouldprobably go undetected if not uncovered at this stage.

5 For example, we examined all regulatory inspection reports for the fivelargest accounting firms in the United States, dating from 2004 to 2007, andfound that thirteen of the 19 reports (68%) identified deficiencies inanalytical review (PCAOB, 2008a). Similarly, the IFIAR (2012) reports thatproblems with analytical review is one of the top recurring themes inregulatory inspection findings internationally.


those with no impact on earnings) as a red flag (Nelson,2009; Quadackers et al., 2014), which may heighten theirsearch for additional errors in the financial statements.

Hypotheses development

Psychological research on distractions and diversionssuggests that diversions inhibit performance. Studies onpersuasion have found that diversions make individualsmore susceptible to agreeing with the arguments of others,as they detrimentally affect comprehension (Baron, Baron,& Miller, 1973; Festinger & Maccoby, 1964; Petty, Wells, &Brock, 1976; Watts & Holt, 1979; Zimbardo, Snyder,Thomas, Gold, & Gurwitz, 1970). Similarly, research exam-ining the Elaboration Likelihood Model (for attitude forma-tion) indicates that diversions make cognitive processingmore difficult, resulting in more peripheral (shallow) infor-mation processing (Petty & Cacioppo, 1986; Street,Douglas, Geiger, & Martinko, 2001). Further, cognitiveresearch suggests that diversions consume an individual’sattention, and since attention is limited, less is availableto process important information (Kahneman, 1973;Sagarin, Britt, Heider, Wood, & Lynch, 2003). In a classicstudy on inattentional blindness, participants viewed avideo of individuals passing around a basketball, and wereinstructed to count the number of passes (Simons &Chabris, 1999). In the video, a person in a gorilla suitwalked through the group, stopped, beat their chest andexited. Notably, over half of the participants never sawthe gorilla, as they were too distracted by the task-at-hand.Diversions also leave individuals feeling less compelled todiscover other issues, due to their focus on what they havediscovered. For example, in successful illusions, magiciansdivert attention to specific distracting items (e.g., smoke,noise, and flashes of light) to occupy an audience’s atten-tion and inhibit their ability to uncover the ‘‘tell’’ of thetrick (e.g., Freudenburg & Alario, 2007; Kuhn & Tatler,2005; Kuhn, Tatler, Findlay, & Cole, 2008).

On the other hand, research on information pursuiteffects in both psychology and accounting suggests thatthe effect of diversions on the performance of professionalauditors may either inhibit or enhance performancedepending upon the context considered. Specifically, theinformation pursuit literature suggests that individuals’beliefs are based on not just the information that supportsthat belief, but also on whether they explicitly and activelysearched a specific area for that information. As a result,searching a highlighted area for information amplifies indi-viduals’ reactions to the information that they find there,above and beyond what their reactions would be based onthe information alone (Bastardi & Shafir, 1998, 2000;Redelmeier et al., 2001). This amplified reaction to informa-tion sought out in a specific area is an information pursuiteffect. In our auditing context, this suggests that divertingauditors directly toward a particular clean area of the auditwill amplify their reaction to the lack of errors found there.Without a diversion from management that highlights aspecific area as high risk, any information pursuit effectsthat may occur would be diluted across accounts, lesseningthe impact of any single financial statement account. In con-trast, if management directs auditors to search a specific,



7


allegedly higher risk audit area, and the search reveals thatthe accounts are clean, auditors would tend to react morestrongly to their findings in those specific accounts thanthey would if they had not been highlighted. The auditorsmay then be more likely to believe that other financial state-ment accounts are clean as well, which would inhibit audi-tors’ performance in detecting earnings managementelsewhere in the financial statements.

Nelson and Tayler (2007) and Smith, Tayler, and Prawitt(2011) provide evidence that information pursuit effectsoccur in accounting and auditing settings. For example,Smith et al. (2011) show that auditors are more persuadedby audit evidence uncovered as a result of an active searchin a specific, highlighted area. Nelson and Tayler (2007)investigate possible causes of information pursuit effects,and find evidence that a process they refer to as reconcili-ation drives the effects. During reconciliation, decisionmakers who proactively pursued specific information willthen process its potential implications within the contextand react more to that information. This reconciliation‘‘potentially renders the pursued information more salient,so the information may demand more attention in thejudgment process’’ (p. 739). Related psychology researchon salience similarly suggests that managers’ highlightinga specific area as high-risk would tend to amplify auditors’reactions to a lack of errors found there (cf. Fiske, 1982;Madan & Spetch, 2012; Taylor & Fiske, 1978). Finally, ifan ostensibly high-risk area of the audit turns out to beclean, auditors may feel more trusting of managementregarding areas of the financial statements not identifiedas risky (cf. Bowlin, Hales, & Kachelmeier, 2009; Bowlinet al., 2014; King, 2002) and search for errors in the restof the financial statements less vigilantly.6

In contrast, a diversion to an ostensibly risky area inwhich errors are discovered by the auditor is likely to havea very different effect on auditor behavior. While prior psy-chology research on distraction suggests that such diver-sions would serve to inhibit the auditors’ effectiveness atuncovering earnings management, the information pursuitliterature and other accounting research suggests thatdiversions to areas in which auditors uncover errors wouldhave the opposite effect. Specifically, just as a purposefulpursuit of evidence in a clean audit area that was specifi-cally highlighted as high risk could amplify auditors’ reac-tions to the lack of errors there, a similar diversion frommanagement that highlights specific accounts with errorswould similarly amplify auditors’ reactions to the errorsthat they do find there (cf. Nelson & Tayler, 2007;Redelmeier et al., 2001). Even though the other errors haveno impact on earnings, they are not neutral with respect tothe audit. By increasing auditors’ reactions to the othererrors, the diversions to the other accounts would increasethe likelihood that the auditors would subsequently

6 In fact, there is evidence of managers attempting to conceal informa-tion by using diversions to clean areas. For example, to perpetuate its fraud,the ZZZZ Best Company management repeatedly used diversionary state-ments that directed auditors to accounts in its legitimate carpet-cleaningsubsidiary in order to divert attention away from its fraudulent restorationbusiness (Knapp, 2010). In addition, managers of HealthSouth and CrazyEddie allegedly concealed fraud from auditors with overt efforts to divertauditors’ attention away from the fraud (Knapp, 2010).


uncover the earnings management. Management’s diver-sion makes those specific accounts more salient, whichwould tend to amplify auditors’ reactions to what they findthere (cf. Fiske, 1982; Madan & Spetch, 2012; Taylor &Fiske, 1978).7

Combining the information pursuit literature with theauditing literature suggests specific ways that divertingauditors to other errors would amplify auditors’ reactionsto them. In particular, Nelson and Tayler (2007) predictand find that proactively pursuing information leads deci-sion makers to consider potential implications of the infor-mation in contextually specific ways, and therefore react tothe pursued information more strongly. The auditing liter-ature suggests contextually specific ways in which thesereactions might occur within our setting. For example, ifmanagement specifically directs auditors to accounts thatthey believe are high risk, and yet apparently did little ornothing to detect and remove any errors there, this diver-sion sends an especially negative signal about manage-ment’s internal controls over its financial statementpreparation (COSO, 2012; PCAOB, 2007), elevating theauditors’ beliefs about the risk of material misstatementin the rest of the financial statements (IFAC, 2009;PCAOB, 2010a). Auditors are trained to exercise profes-sional skepticism (Nelson, 2009; Quadackers et al., 2014;Smith & Kida, 1991), suggesting that auditors would reactto a diversion from management to errors that the clientdid not detect and remove as a significant red flag for con-trols over the financial statements as a whole. If auditorsreact to the discovery of other errors as a red flag, thenhaving been directed to a specific audit area to search forsuch errors will likely strengthen their reaction to findingthem (cf. Nelson & Tayler, 2007; Redelmeier et al., 2001).And, given that the reaction prescribed by audit standardsto uncovering such errors is to heighten concerns regard-ing the risk of errors elsewhere in the financial statements,it is likely that auditors will be more vigilant in their searchfor errors in the rest of the financial statements.

Thus, while prior psychological research on distractionssuggests that diversions would be generally effective atconcealing earnings management, the information pursuitliterature in psychology and accounting, as well as otheraccounting research and practice, suggests that the effectof diversions would be quite different depending onwhether auditors are diverted to clean accounts or toaccounts containing other errors. Specifically, the diversionfrom management to other accounts would tend to amplifyauditors reactions to what is (or is not) in those otheraccounts. Without a diversion to the other accounts, audi-tors would be (if anything) more likely to detect earningsmanagement elsewhere in the financial statements when

Theory does not require that the diversions impact the rate at whichthe other errors are discovered in order to influence auditors’ reactions tothem. Even if the other errors are discovered relatively easily and at just ashigh of a rate without the diversion there, information pursuit theory stillpredicts a stronger reaction to what is in those other accounts when theyare specifically directed to search there. Further, when no other errors arepresent, diversions also cannot impact the rate at which the other errors arediscovered (since there are none to be discovered). Yet information pursuittheory still predicts a stronger reaction to the lack of errors with a diversionto those accounts.




other errors are present. However, this difference would bemore likely to be significant when management specifi-cally directs auditors to search those other accounts, basedupon information pursuit effects in accounting and psy-chology, as well as other attributes of the auditing settingdiscussed above. This does not preclude the possibility ofa smaller reaction to the other errors without managementhighlighting the accounts that they are in. However, thestrongest a priori case for a reaction to the other errors(or to a lack of other errors) in the financial statementswould be when management explicitly directs auditorsto search those specific accounts. The different reactionsto the errors (or lack of errors) in those other accountswould then affect the vigilance with which auditors searchthe rest of the financial statements, and, therefore, the like-lihood of uncovering the earnings management error.

Taken together, this discussion suggests that auditorswould be the least likely to uncover earnings managementwhen clients divert auditors’ attention to accounts that areclean, and most likely to uncover earnings managementwhen clients divert auditors’ attention to accounts thatcontain errors. Auditors’ earnings management detectionwould be between these highest and lowest levels whenthere are no diversions to the other errors, as well as whenthere are no diversions and no other errors. This suggeststhe following hypothesis:

H1. An auditor’s detection of earnings management willbe lowest when diverted to clean accounts, highest whendiverted to accounts containing other errors, and inbetween these levels either when there are no diversionsto clean accounts, or when there are no diversions toaccounts containing other errors.

9 Note that while errors can cause unusual fluctuations, financialstatement fluctuations can also be the result of non-error causes. As aresult, our study more broadly tests diversions from unusual fluctuations asmuch as it tests diversions from earnings management. Because auditorsrely heavily on detection of unusual fluctuations during analytical reviewas a major source of substantive testing, failure to detect unusual

Method

Participants

A representative from each of the Big Four and otheraudit firms identified auditors with sufficient knowledgeto perform the task. Seventy-six auditors, with an averageof four years of audit experience, took part in the study.8

Neither experience nor rank significantly influences ourresults. Our participants’ experience is consistent with priorresearch on analytical review (see Messier et al., 2013 for areview).

Overview of the study

The experiment required that auditors complete analyt-ical review procedures on the financials statements of a

8 The original participant pool contained 77 auditors; however, one wasremoved due to a software error. Approximately 66% of participants wereemployed by Big Four firms, while 28% were employed by regional firms.Firm type does not significantly influence our results. Approximately 22%were associate or staff auditors (averaging 1.4 years of audit experience),61% were senior or in-charge auditors (averaging 3.5 years of experience),7% were managers or senior managers (averaging 6.1 years of experience),4% were partners (averaging 18.7 years of experience), and 7% did notspecify an auditor rank (averaging 3.7 years of experience).


hypothetical client. Following prior studies (e.g., Asareet al., 2000; Asare & Wright, 2003; Bedard & Biggs,1991a, 1991b; Bhattacharjee, Kida, & Hanno, 1999; Cohen& Kida, 1989; Luippold & Kida, 2012; Moreno et al.,2007), we adapted a set of financial statements with stableaccount balances over time, and then seeded an accountingerror into the accrual of compensation expense that wouldunderstate current-year expenses and accruals by approx-imately $450,000. The error caused unusual and materialfluctuations in these accounts for auditors to detect duringtheir analytical review.9 The compensation expense errorwas divided evenly into administrative compensationexpense and sales compensation expense, understating bothby approximately $225,000. The accrual entry was dividedevenly between current and non-current accrued compensa-tion.10 Background data, provided prior to beginning theanalytical review, revealed that the company beat analysts’forecasted EPS by approximately $0.025/share (net incomewas about $8.45 million). If the compensation error was dis-covered, the company would miss its earnings target.

The study employed a 2 � 2 experimental design. Thefirst independent variable manipulated whether manage-ment provided a diversionary statement. The diversionarystatement involved management explicitly identifying riskelsewhere in the financial statements in an attempt to lurethe auditor away from managed earnings. In the diversion-ary statement conditions, the client’s background informa-tion indicated that the individual responsible formaintaining non-current assets (i.e., property, plant andequipment, intangibles and other non-current assets) leftthe company about six months ago. It also stated thather replacement transferred in from the manufacturingfloor and has very little accounting experience. Aside fromthat change, the auditors were told that there was no otherturnover with any of the accounting personnel responsiblefor financial reporting. In the other conditions, no specificarea of risk was identified.

The second independent variable was manipulated byseeding two other, offsetting errors in non-current assetsthat had no impact on earnings but created unusual fluctu-ations for auditors to detect. One error concerned the com-pany failing to record a portion of depreciation expense forfurniture and fixtures, resulting in an understatement ofdepreciation expense and accumulated depreciation byapproximately $450,000. The other error overstated

fluctuations elevates the risk of undetected misstatements (Messier,Glover, & Prawitt, 2012). We are particularly concerned here with theimplications of our findings for audits when management deliberatelyconceals earnings manipulations. As Bell et al. (2005) note, those auditscarry the largest social costs, and the effectiveness of various tactics thatmanagers could use is not well understood by prior research.

10 The error was divided into different accounts so that it was moredifficult to uncover. While compensation is often a current accrued liability,non-current accrued compensation can relate to post-retirement benefits,deferred incentive compensation, pension benefits, non-expiring vacation/sick time, etc.



Download and Install Program

Enter Pin Code and

Name

Read Instructions

Read Background Information

Conduct Analytical

Review

Answer Post Experimental

Questions

Email Results

File

Record Error

Judgments

Fig. 1. Timeline of experimental procedures. The experiment was conducted through a computer program that participants downloaded and completed attheir convenience. Participating auditors received an email with download instructions and a pin-number to grant them access to the program. Afterdownloading the program, they entered their name and pin number. They then read instructions and background information before beginning theanalytical review. During the review, auditors searched through several pages of information related to the client’s financial statements. If participantsidentified an error, they could navigate to a page to record their judgment as part of their evaluation. Participants could make as many error judgments asneeded throughout the review, and they could revert back to the background information as necessary. Upon completing the analytical review, auditorsanswered post experimental questions. At this stage, they could not go back to the analytical review. After completing the study, participants emailed thefile back to the experimenters.


amortization expense, and hence understated the net valueof goodwill by approximately $450,000.11 These othererrors were embedded into the same area that the diversion-ary statement pointed to (i.e., non-current assets). When theother errors were not present, none of the accounts relatedto depreciation and amortization reflected any material fluc-tuations from previous years.

These manipulations resulted in four conditions (i.e.,whether or not there were diversions to fixed assets, andwhether or not there were other errors in fixed assets).The 2 � 2 fully crossed design allows us to test the effectsof diversionary statements with or without other errors inthe diversionary accounts.

13

Procedures

The study was administered through a computer pro-gram. After agreeing to participate, the auditors receivedan email with the relevant information to access the pro-gram (see Fig. 1 for the timeline of the experiment).12 Uponstarting the study, the participants were randomly assignedto a condition and navigated through a set of instructionsand background information about the company, its indus-try, its position in the market, and details about its audit his-tory. Participants were told that they were the senior-in-charge on an audit of a manufacturing company. The com-pany had consistently met analysts’ earnings forecasts, andanalysts had recently forecasted income to remain at $8.2

11 To ensure that the earnings management error was more difficult todetect than the other errors, five individuals with a mean of over threeyears of audit experience rated the three errors on a 10-point scale, whichranged from very easy (one) to very difficult (ten). Significant differenceswere found between the earnings management error (6.4) and the othererrors (amortization = 2.6, p = 0.002 and depreciation = 3.4, p = 0.008),suggesting that the earnings management was more difficult to uncoverthan the other errors.

12 There was no time limit to the task, and we find no significant effects oftime on our results.


million (or $0.82 per share), which was the same as the pre-vious two years. In addition, the materiality threshold forthe audit was explicitly stated to be $100,000, which madematerial the fluctuations for all of the accounts affected bythe earnings management and the other errors.

After reading the instructions and background, theauditors began the analytical review. At this stage, partici-pants were exposed to information from the client thatcompared the unaudited financial balances of the currentyear to the audited balances of the previous two years.13

Navigation buttons allowed the participants to access all ofthe financial details. In total, twenty pages of informationpresented the balance sheet, income statement, statementof cash flows, and additional detailed information which fur-ther described the balances on the financial statements.14

For example, one page provided details for accounts receiv-able, such as gross and net accounts receivable balances,allowance for doubtful accounts, bad debt expense, an aginganalysis and key financial ratios. The navigation buttonswere always present on the left side of the screen duringthe analytical review, so that participants could move freelyto any piece of information in any order they preferred.

A button on each screen labeled ‘‘Record Judgment’’brought the auditors to a page where they could recordany unusual fluctuations that they identified in a freeresponse text box. They could return to the ‘‘Record Judg-ment’’ page as often as they wanted to add new judgments.

The financial statements were created from several other accountingstudies using similar analytical review procedures, including Bhattacharjeeet al. (1999), Cohen (1994), Cohen and Kida (1989), and Moreno et al.(2007).

14 In addition to the balance sheet, income statement and cash flowstatement, participants could access details for the following accounts:marketable securities, accounts receivable, inventory, cost of goods man-ufactured/sold, property, plant and equipment, prepaid expenses, accumu-lated depreciation, intangibles, other non-current assets, accounts payable,other current liabilities, debt, other non-current liabilities, equity, sales,selling expenses and administrative expenses. In addition, a navigationbutton allowed participants to revisit the background information.



0.19

0.27

0.20

0.25

0.30


Each time they returned, all of their previous entries werelisted numerically in the order they were entered. Anotherbutton allowed them to finish the exercise, and broughtthem to supplemental questions (i.e., demographic infor-mation, questions pertaining to professional skepticism,etc.). Participants emailed the results file back when theycompleted the study.

0.04

0.17

0.00

0.05

0.10

0.15

No Diversionary Statement Diversionary Statement

Results

We first test our hypotheses about the effects of diver-sions (to accounts with or without other errors) on anauditor’s identification of an earnings management error.As a supplemental analysis, we then examine whetherauditor skepticism affects an auditor’s detection of man-aged earnings.

No Other Errors Other Errors

Fig. 2. The effect of diversions to accounts with or without other errorson task performance. Task Performance ¼ Identification of Earnings Management

1þNumber of Irrelevant Judgments .

16 This task performance measure scales detection of the compensationerror by the number of erroneous judgments listed, so that the measurereflects both audit effectiveness and efficiency. Identifying a larger numberof additional irrelevant accounts diverts subsequent audit testing awayfrom higher risk accounts, risking both audit effectiveness and efficiency(Messier et al., 2012). For instance, an auditor who identified thecompensation error without any irrelevant judgments would receive ascore of 1.00. An auditor who listed three irrelevant judgments along withthe compensation error would receive a score of 0.25. An auditor who failedto indicate the compensation error would receive a score of 0.00, regardlessof the number of irrelevant judgments listed.

17 Neither of our dependent variables (Task Performance or the percentageof auditors identifying earnings management) includes identification of theother errors in their calculation. Libby and Frederick (1990, 360) and Libby(1985, 660–661) design their dependent variables this way for similarreasons. Specifically, had we also included detection of the other errors in

Dependent variables

We use two dependent variables to proxy for theidentification of earnings management. The purpose ofanalytical review is to highlight accounts with unusualfluctuations in order to focus the audit team’s subsequentattention and effort where the risk of material misstate-ment is highest. Since the experiment involved a realisticanalytical review task, which allowed the auditors to listas many potential errors as they felt necessary, partici-pants could simply list many areas that they believemay contain an error (i.e., take a ‘‘shotgun’’ approach).While identifying a large number of accounts for investi-gation should, in theory, increase the likelihood of detect-ing the earnings management error, flagging additionalirrelevant accounts ultimately diverts the audit team’ssubsequent attention away from the accounts that con-tain the highest risk of misstatement to other accountsthat do not. As such, each additional account identifieddilutes the amount of resources the audit team cancommit to locating the earnings manipulation (i.e., thecompensation error). This could affect not only auditefficiency, but also effectiveness, because it ultimatelyreduces the audit team’s attention to the area containingthe managed earnings.15

Because of this effect, we use two alternative dependentvariables for our tests of H1. Our first dependent variable(Task Performance) places identification of the earningsmanagement account into the context of how many irrele-vant accounts were also identified. It is calculated asfollows:

Task Performance¼ Identification of Earnings Management1þNumber of Irrelevant Judgments

ð1Þ

The numerator (Identification of Earnings Management) iscoded correct (1) if the auditor identified one or more of

15 Our approach is consistent with studies on brainstorming (e.g.,Bellovary & Johnstone, 2007; Carpenter, 2007; Hammersley, 2011;Trotman, Simnett, & Khalfia, 2009). It is also consistent with our interviewsof audit partners, who indicated that good performance in analytical reviewentails not merely highlighting high risk fluctuations, but doing so withoutflagging other accounts that do not contain unusual fluctuations.


the affected compensation accounts (or listed compensa-tion or an appropriate synonym), and incorrect (0) other-wise. The number of irrelevant judgments reflects thenumber of accounts (or areas) identified by the auditor aspotentially containing an error where no unusual fluctua-tion actually existed. For instance, if a participant listedinventory, accounts receivable and leases as accounts pos-sibly containing errors, then three irrelevant judgmentswould be recorded.16

Our second dependent variable is the percentage ofauditors who identified one or more of the affected com-pensation accounts (or listed compensation or an appropri-ate synonym) in their responses. This dependent variableshows the proportion of auditors who named the earningsmanagement account, but does not account for whetherthey also highlighted a large number of other, irrelevantaccounts.17

the calculation of our dependent variables, results consistent with H1 couldhave then been merely attributed to participants in the ‘‘no other errors’’conditions having fewer errors available for detection to begin with, givingan unfair advantage in the dependent variable to those in the ‘‘other errors’’conditions. Instead, our dependent variables are consistent with our focuson testing the effects of diversions on the detection of earnings manage-ment elsewhere in the financial statements. Roediger, Stellon, and Tulving(1977) discuss the methodological importance of this approach.



Table 1The effect of diversions to accounts with or without other errors on taskperformance.a

Diversion

No Yes

Panel A: Means, (standard deviations), sample sizeOther errors

No 0.19 (0.37) 0.04 (0.09)n = 19 n = 21

Yes 0.17 (0.32) 0.27 (0.38)n = 19 n = 17

Source SS df MS F p

Panel B: ANOVAOther errors 0.21 1 0.21 2.16 0.146Diversion 0.01 1 0.01 0.11 0.739Diversion � other errors 0.31 1 0.31 3.28 0.074Error 6.84 72 0.09

Total 7.37 75

Teststatistic

pb

Panel C: Test of hypothesesDiversion to clean accounts lowest, diversion

to other errors highest, and no diversionto the other errors in between

J = 1.96 0.025

Diversion to clean accounts lowest, diversionto other errors highest, and no diversionwith no other errors in between

J = 1.91 0.028

a Task Performance ¼ Identification of Earnings Management1þNumber of Irrelevant Judgments .

b These tests have directional predictions, and their p-values are one-tailed.

18 In addition to their use of the Jonckheere-Terpstra test, Fanning et al.(2015), Sedor (2002) and Arel, Jennings, Pany, and Reckers (2012) also usedcell contrast weights with a rank-ordering reflecting their expectations. Weuse this approach as well, as additional robustness tests of H1. For example,contrast weights of +0.5, +0.3, +0.2, �1 could be assigned, (respectively) tothe diversion to other errors condition, the no diversion to other errorscondition, the no diversion and no other errors condition, and the diversionto clean accounts condition, as an alternative test of our theory andhypothesis. Compared to contrast weights, the Jonckheere-Terpstra test hasthe advantage of being a direct test of the rank-ordering of the cell meanswithout also requiring arbitrary decisions about the relative magnitude ofthe contrast weights (Fanning et al., 2015). Because contrast weightsrequire arbitrary decisions about the magnitude, they should be conductedas sensitivity analyses, using different sets of weights that all reflect thebasic rank-ordering to be tested (e.g., Arel et al., 2012; Sedor, 2002).Consequently, we tested H1 using contrast weights of (+0.5, +0.3, +0.2, �1),(+1, �0.2, �0.3, �0.5), (+0.75, +0.13, +0.12, �1), and (+1, �0.12, �0.13,�0.75). In all cases, results are supportive of H1 (all p’s 6 0.032).

19 This test (t = 1.811, p = 0.037) is the one-tailed t-test associated withthe F-test of the Diversion � Other Error interaction (F = 3.28, p = 0.074) inTable 1 (e.g., Kachelmeier & Williamson, 2010).

20 In addition, the 0.04 task performance score in the diversionarystatement without errors condition is significantly less than the averagescore of 0.21 for the rest of the conditions (t = �2.16, p = 0.017). Also, the0.27 score for auditors diverted to accounts with errors is higher than the0.13 average score of the remaining conditions (t = 1.64, p = 0.053) (seeKadous, Koonce, & Towry, 2005).


Hypothesis tests: task performance

Fig. 2 shows the means of Task Performance by experi-mental condition. H1 predicts that auditors’ detection ofearnings management will be lowest when auditors arediverted to clean accounts, highest when auditors arediverted to other accounts that contain errors, and inbetween these levels when auditors are not diverted tothe other errors, as well as when there are no diversionsand no other errors.

Predictions of this type are tested by a Jonckheere-Terpstra test (e.g., Abarbanell, 1991; Fanning, Agoglia, &Piercey, 2015; Frederickson, Hodge, & Pratt, 2006; Libby& Lipe, 1992; Libby & Trotman, 1993; Phua, Abernathy, &Lillis, 2011; Sedor, 2002; Tan & Jamal, 2006). As Fig. 2shows, the mean Task Performance score of auditors inthe Diversion, No Other Errors cell is the lowest at 0.04, fol-lowed by the two No Diversion cells, with and withouterrors (0.17 and 0.19, respectively), followed by the Diver-sion, Other Errors cell (0.27). As Table 1 shows, a Jonckhe-ere-Terpstra test shows that the predicted rank-orderingsare statistically significant. Specifically, task performancein detecting earnings management was lowest when audi-tors were diverted to clean areas, highest when auditorswere diverted to accounts that contained other errors,and in between these highest and lowest levels when audi-tors were not diverted to the other errors (J = 1.96,p = 0.025), as well as when they were not diverted and


there were no other errors (J = 1.91, p = 0.028). Theseresults support H1.18

We perform supplementary analyses of our TaskPerformance variable using ANOVA. Table 1 shows aDiversion � Other Errors interaction significant atp = 0.074. Consistent with information pursuit effects,the interaction shown in Fig. 2 suggests that divertingauditors to the other accounts amplifies their reactionto what is (or, when the other errors are not present,to what is not) in those other accounts. Specifically,when no diversions are used, auditors’ did not react tothe presence or absence of the other errors (0.17 vs.0.19, respectively, t = �0.24, p = 0.810). In contrast, whendiverted to the other accounts, auditors’ reacted signifi-cantly to the presence or absence of the other errors(0.27 vs. 0.04, respectively, t = 2.31, p = 0.012). Auditors’reactions to the errors (or lack of errors) in the otheraccounts were significantly larger with the diversion tothose accounts than without (t = 1.81, p = 0.037).19 Thesesupplementary results are consistent with our theory andhypothesis.

As an additional analysis, we also tested whether thetask performance score in each condition was significantlydifferent from zero. The scores in the two no diversion con-ditions, as well as in the diversions to accounts with errorscondition, were all significantly different from zero(p’s 6 0.010). On the other hand, the diversionary state-ment without other errors was not different from zero(p = 0.549). These findings suggest that when auditors arediverted to areas that do not contain errors, they are notlikely to uncover earnings management elsewhere in thefinancial statements, compared to auditors in the otherexperimental conditions.20

These results suggest that if management alerts audi-tors to risk in accounts that are ultimately clean, thisdiversionary tactic appears to be effective at divertingthe auditor away from managed earnings. That is,



29.4%

6.7%

29.4%

43.8%

0%

10%

20%

30%

40%

50%

No Diversionary Statement Diversionary StatementNo Other Errors Other Errors

Fig. 3. The effect of diversions to accounts with or without other errorson the percent of auditors identifying earnings management. Thisanalysis includes auditors who listed fewer than five irrelevant judg-ments to limit the impact of taking a shotgun approach. Responses werecoded correct if any of the recorded judgments identified the affectedcompensation accounts, or listed the word compensation or an appro-priate synonym, and incorrect otherwise.

Table 2The effect of diversions to accounts with or without other errors on thepercent of auditors identifying managed earnings.a

Diversion

No Yes

Panel A: Percentages, sample sizesOther errors

No 29.4% 6.7%n = 17 n = 15

Yes 29.4% 43.8%n = 17 n = 16

Teststatistic

pb

Panel B: Hypothesis testsDiversion to clean accounts lowest, diversion

to other errors highest, and no diversionto the other errors in between

J = 2.29 0.011

Diversion to clean accounts lowest, diversionto other errors highest, and no diversionwith no other errors in between

J = 2.29 0.011

a This analysis includes auditors who listed fewer than five irrelevantjudgments to limit the impact of taking a shotgun approach. Responseswere coded correct if any of the recorded judgments identified theaffected compensation accounts, or listed the word compensation or anappropriate synonym, and incorrect otherwise.

b These tests have directional predictions, and their p-values are one-tailed.


auditors appear to be relatively ineffective at detectingmanaged earnings in other areas of the audit whendiverted to a clean account. However, if managementovertly leads auditors to an area containing errors, audi-tors perform better at discovering managed earnings else-where in the financial statements. Overall, our resultsappear to be consistent with information pursuit theory.That is, directing auditors to search other accountsappears to amplify their reactions to what either is or isnot in those other accounts.21

Hypothesis tests: percentage of auditors identifying earningsmanagement

We also test our hypotheses on the percent of audi-tors who detected the unusual fluctuations created bymanaged earnings.22 To limit the effect of auditors

21 Note that, even if auditors discovered the other errors at just as high ofa rate without being diverted there, information pursuit theory stillsuggests that diversions would amplify auditors’ reactions to the othererrors (or to the lack of errors) in those accounts. In fact, our findings areconsistent with this. When the other errors were present, auditors detectedthe other errors there just as much without the diversion as they did with it(auditors found on average 1.4 vs. 1.5 of the other errors, t = 0.39, p = 0.70).When the other errors were not present, diversions also did not impactauditors’ detection of the other errors (since there were none to be found).Yet, our tests of H1 (and the supplemental interaction test) suggest thatauditors still reacted more to the other errors (or to the lack of errors) in theother accounts when they were diverted there by management (Table 1,Panels B and C), consistent with information pursuit theory. In theinformation pursuit literature, Nelson and Tayler (2007) and Smith et al.(2011) discuss the methodological importance that participants acquireinformation at approximately the same rate when testing whether theexplicit information pursuit (e.g., the diversions to other accounts)amplifies their reactions to it.

22 Again, earnings management was coded as correct if the auditoridentified any of the affected compensation accounts (or listed compensa-tion or an appropriate synonym), and incorrect otherwise.


identifying managed earnings as a result of simply takinga shotgun style approach (i.e., by listing many accounts),we first analyzed the judgments of auditors who identifiedfewer than five irrelevant errors. We use this cutoffbecause, as we previously discussed, large numbers ofirrelevant judgments imply that auditors are inefficient,as they may pursue false leads. In addition, large numbersof irrelevant judgments suggest that the auditor may notactually have detected a likely instance of earnings man-agement, but simply included the relevant accounts bychance while generating a laundry list of accounts toexamine. A valid measurement of earnings managementdetection would not count these chance inclusions of therelevant accounts. We selected the moderately low cutoffof fewer than five irrelevant items so as to clearly distin-guish those including many irrelevant items in theirresponses (and thus likely using a shotgun approach) fromthose including very few (and thus less likely to be usingone).

As can be seen in Fig. 3, only 6.7% of auditors whowere diverted to clean accounts uncovered managedearnings, while 43.8% of auditors uncovered earningsmanagement when they were diverted to accounts thatcontained the other errors. In contrast, when auditorsreceived no diversions to fixed assets (either with orwithout the other errors), 29.4% detected the earningsmanagement. Jonckheere-Terpstra tests of H1 indicatethat auditors’ earnings management detection was low-est when diverted to clean accounts, highest whendiverted to the other errors, and in between when nodiversion was used, with or without the other errors(in both tests, J = 2.29, p = 0.011; Table 2). These resultssupport H1.



Table 3The effect of diversions to accounts with or without other errors on the percent of auditors identifying managed earnings: sensitivity to number of irrelevantjudgments.a

Irrelevant judgmentsincludedb

N No other errors Other errors Tests of H1

Diversion (A) (%) No diversion (B) (%) No diversion (C) (%) Diversion (D) (%) A < B < D(p-value)c

A < C < D(p-value)c

All 76 23.8 31.6 31.6 47.1 0.070 0.070<13 75 23.8 33.3 31.6 47.1 0.070 0.070<12 75 23.8 33.3 31.6 47.1 0.070 0.070<11 74 20.0 33.3 31.6 47.1 0.042 0.042<10 74 20.0 33.3 31.6 47.1 0.042 0.042<9 74 20.0 33.3 31.6 47.1 0.042 0.042<8 74 20.0 33.3 31.6 47.1 0.042 0.042<7 71 15.8 29.4 33.3 47.1 0.022 0.023<6 68 11.8 29.4 33.3 43.8 0.022 0.023<5 65 6.7 29.4 29.4 43.8 0.011 0.011<4 61 7.7 25.0 25.0 43.8 0.015 0.015<3 55 8.3 28.6 30.8 43.8 0.022 0.024<2 42 0.0 25.0 25.0 50.0 0.007 0.007None 27 0.0 37.5 25.0 60.0 0.018 0.015

a Responses were coded correct if any of the recorded judgments identified the affected compensation accounts, or listed the word compensation or anappropriate synonym, and incorrect otherwise.

b An irrelevant judgment relates to a clean area that an auditor identified as containing an error.c As before, we used the Jonckheere-Terpstra test to test the rank order of the experimental conditions indicated in the column headings of this table.

They all have directional predictions, and their p-values are one-tailed.


Fig. 3 suggests an interaction in this dependent vari-able similar to the interaction that we observed in thesupplementary analysis of our Task Performance depen-dent variable. Specifically, Fig. 3 suggests that divertingauditors to the other accounts appears to amplify theirreaction to the errors (or to the lack of errors) in thoseother accounts, consistent with information pursuiteffects. To test this interaction, we employ a binomiallogistic regression model with Diversion, Other Errors,and their interaction as independent variables. We findthat the interaction shown in Fig. 3 is statistically signif-icant, as expected. Specifically, auditors’ detection of theearnings management is more influenced by the presenceor absence of errors in the other accounts when divertedto those accounts by management (v2 = 3.55, p = 0.030).23

Overall, our findings suggest that directing auditors toclean accounts can be an effective audit management tool,but directing auditors to accounts containing other errorsmay not only be ineffective, it may actually have the oppo-site effect on management.

Further, we performed a sensitivity analysis to deter-mine how the number of irrelevant judgments impactsthe percent of auditors uncovering earnings manage-ment. As shown in Table 3, the results for H1 replicatewhen we include auditors who are not simply using ashotgun approach (i.e., naming a large number of irrele-vant accounts), and become more significant as the

23 In addition, auditors who were exposed to a diversionary statementwhich led them to an error-free account were significantly less likely todiscover earnings management than the remainder of the sample (6.7%compared to 34.0%, v2 = 4.31, p = 0.019), while auditors exposed to adiversionary statement which led them to an account containing errorsperformed significantly better than the remainder of the participants(43.8% compared to 22.4%, v2 = 2.73, p = 0.049).


number of irrelevant items included in the analysesdecreases.24

Thus, it appears that the effect of diversions (to accountswith and without other errors) on the proportion of audi-tors uncovering earnings management are robust acrossthe number of irrelevant judgments.

Auditor skepticism

We also investigated the impact that skepticism mayhave on an auditor’s detection of earnings management.As part of the post-experimental questionnaire, auditorswere asked to indicate their level of agreement with fourstatements (shown in Table 4) reflecting a skeptical out-look toward management on six-point scales (rangingfrom strongly disagree to strongly agree).

These measures of skepticism capture auditors’ ten-dency to adopt a presumptive doubt perspective on man-agers’ representations (Bowlin et al., 2014; Nelson, 2009;Quadackers et al., 2014). Compared to a more neutralperspective of professional skepticism (e.g., Hurtt, 2010),a presumptive doubt view focuses specifically on aheightened assessment of the risk that management ismisleading (Nelson, 2009). Because a presumptive doubtview would tend to indicate an elevated alertness tored flags, Quadackers et al. (2014) suggest that auditors’

24 As previously mentioned, we used a cutoff of less than 5 irrelevantjudgments to test H1 (Fig. 3 and Table 2). The sensitivity analysis in Table 3show that the results of H1 are robust to this and other cutoffs we couldhave chosen to control for auditors adopting a shotgun approach. Inparticular, the cell means in Table 3 suggest the choice of cutoff makes verylittle difference to three out of our four experimental conditions. Thedifferent cutoffs appear to have the largest influence on the ‘‘diversions, noother errors’’ condition (where detection is the lowest and therefore ashotgun approach would be expected to have the largest impact).



Table 4The impact of skepticism on task performance.

Individual statementa % of auditorsb Task performancec

Disagree(less skeptical) (%)

Agree(more skeptical) (%)

Disagree(less skeptical)

Agree(more skeptical)

t-Stat. p-Valued

Percent of auditors classified as more or less skeptical and task performance [means, (standard deviations) and t-tests]c

Earnings management 18.5 81.5 0.06 0.19 2.13 0.020(0.15) (0.34)

Management fraud 63.2 36.8 0.10 0.27 2.00 0.027(0.23) (0.40)

Hide errors 23.7 76.3 0.06 0.20 2.60 0.006(0.11) (0.35)

Distract auditors 75.0 25.0 0.12 0.28 1.59 0.062(0.27) (0.40)

Presumptive doubt composite scoree 56.9 43.1 0.07 0.26 2.26 0.015(0.19) (0.41)

a Auditors responded to the following four statements pertaining to skepticism of management:� Managers try to manage earnings to meet earnings targets.� Managers are likely to commit fraud if left unmonitored.� Managers may try to hide an error in the financial statements to meet an earnings target.� Managers try to distract auditors with easily detectable errors in hopes of hiding other errors.

For each statement, auditors were asked to indicate their agreement on a six-point scale, ranging from strongly disagree to strongly agree. Scores rangingfrom one through three indicated disagreement (i.e., less skeptical of management), while scores of four through six indicated agreement (i.e., moreskeptical of management).

b The sample consists of 76 auditors for the individual questions. The composite skepticism score includes the 65 auditors who were either above orbelow the midpoint (more or less skeptical, respectively).

c Task Performance ¼ Identification of Earnings Management1þNumber of Irrelevant Judgments .

d These contrasts all test directional predictions, and their p-values are one-tailed.e The skepticism composite score was calculated by taking the mean response to the four statements. Auditors who were above the midpoint generally

agreed with the statements and were considered to be more skeptical of management, while those below the midpoint disagreed and were considered to beless skeptical. Eleven individuals fell exactly on the midpoint and were removed from the analysis.


individual tendencies to adopt a presumptive doubt vieware likely to be more associated with a heightenedawareness of the risk of material misstatement than theirtendencies toward a neutral view. They find that similarpresumptive doubt measures of auditor skepticism aremore positively associated with audit risk assessmentsand planning judgments, especially when audit risk ishigh. Using a similar expectation, we use our measuresto test whether this association between a presumptivedoubt view and auditor behavior extends beyond riskassessment and planning judgments to actual detectionof earnings management embedded in the financialstatements.

We used these responses to create a presumptive doubtcomposite score (mean response to the four statements)for each auditor, and divided the auditors into low andhigh skepticism groups using a midpoint split. Auditorswho were above the midpoint generally agreed with thestatements and, thus, were considered to be more skepti-cal, while those below the midpoint generally disagreedand were considered to be less skeptical.25

25 Sixty-five auditors were classified as either more skeptical or lessskeptical after dichotomizing the skepticism composite score. Elevenauditors scored exactly on the midpoint (3.5) and were removed fromthe composite analysis. Those classified as more skeptical had an averagecomposite score of 4.2, which was significantly higher than the 2.9 score forthose classified as less skeptical (p < 0.01). Furthermore, more skepticalauditors were more in agreement with each individual skepticism state-ment than less skeptical auditors (all p-values < 0.01).


As can be seen, the majority of auditors agreed thatmanagers try to manage earnings and that they may tryto hide errors to meet earnings targets (81.5% and 76.3%agreed, respectively). Fewer auditors agreed that managersare likely to commit fraud if left unmonitored and try todistract auditors with easily detectable errors in hopes ofhiding other errors (36.8% and 25.0% agreed, respectively).It appears that most auditors are generally skeptical ofmanagement with respect to managing earnings, and someto the point where they think managers would act in afraudulent or overtly deceitful manner.

While auditors are trained to practice professionalskepticism, there appears to be differences in the degreeof skepticism held by auditors. As a result, we tested theprediction that auditors with a greater tendency towardpresumptive doubt would be more likely to scrutinizethe financial statements (cf. Quadackers et al., 2014)and perform better at detecting managed earnings. Thecomposite score in Table 4 indicates that auditors withmore presumptive doubt are associated with greater taskperformance scores than those who were not as skeptical(0.26 compared to 0.07, p = 0.015), providing evidence ofan association between a presumptive doubt view andearnings management detection. Auditors’ agreement toeach individual statement yielded similar results(p’s 6 0.062, Table 4). We also examined the effects ofskepticism on our binary dependent variable, Identifica-tion of Earnings Management. That is, we examinedwhether skepticism is related to the percent of auditors




who identified managed earnings. Again, we found apositive relationship between the level of skepticism inauditors and the likelihood of discovering earnings man-agement (v2 = 2.35, p = 0.063). Moreover, when we condi-tion these analyses on the number of irrelevant itemsidentified (similar to Table 3), these results are significantfor all analyses that included fewer than eleven irrelevantjudgments (p’s 6 0.034).26

Like Quadackers et al. (2014), we use measured auditorskepticism variables to test for an association with mea-surements of auditor behavior, and, as a result, show corre-lation rather than causation. For example, just as ourfindings suggest that more skeptical auditors may be morelikely to discover earnings management, it could be thatthe auditors who discovered the earnings managementwere then more likely to indicate higher levels of individ-ual skepticism. Still, while correlation is not a sufficientcondition to establish causality, it is a necessary condition,and potentially informative to future research (cf.Quadackers et al., 2014). Future researchers interested inthe effects of individual auditor skepticism traits on audi-tor behavior may wish to investigate the association thatwe document further. Given the variability that we findin the level of skepticism reported across auditors, therecould be an opportunity for enhanced training on profes-sional skepticism, which could be associated with moreeffective earnings management detection.

Conclusion

This study examines the impact of diversions towardeither clean accounts or to accounts with other errors onan auditor’s discovery of earnings management. By doingso, we investigate whether managers manipulating earn-ings can influence the effectiveness of auditor judgmentsand overall audit quality. The results suggest that diver-sions to clean accounts and diversions to accounts contain-ing other errors have different effects on auditors’detection of earnings management. Specifically, we find

26 When we add the composite presumptive doubt score as a covariate tothe Task Performance score ANOVA, the covariate is significantly positive atp = 0.006, and our test of the interaction remains significant at p = 0.043.Furthermore, when we include the composite presumptive doubt score as acovariate when testing our binary dependent variable with binary logisticregressions, the covariate is significantly positive and we obtain similarresults for the interaction term. As a covariate, skepticism does not vary byexperimental condition (p = 0.74). We also expanded this ANCOVA to testfor interactions involving the composite presumptive doubt score. Whenwe use our Task Performance score as the dependent variable, our originaltwo-way diversionary statement � other errors interaction remains signifi-cant (p = 0.045), as well as the main effect of the composite presumptivedoubt score (p = 0.005). We also find evidence of an additional three-wayinteraction (i.e., diversionary statement � other errors � presumptive doubt;i.e., p = 0.017). The three-way interaction shows that the primary interac-tion of interest (i.e., diversionary statement � other errors) is more likely tooccur among more skeptical auditors than among less skeptical auditors,whose Task Performance scores tend to be uniformly low. We do not detectevidence of this three-way interaction using our binary dependent variable.Thus, we find robust evidence that less skeptical auditors are less likely todetect earnings management, and some preliminary evidence that theeffects of the diversionary tactics we test have the most impact on moreskeptical auditors, since less skeptical auditors are unlikely to find theearnings management regardless.


that earnings management detection was worst whenauditors were diverted to clean accounts, best whendiverted to accounts containing other errors, and inbetween these levels when auditors were not diverted toother accounts, with or without other errors.

The study provides several contributions to the litera-ture. Most importantly, it demonstrates that simply divert-ing auditors to clean accounts can deter them from findingmanaged earnings, resulting in a reduction of both auditand financial reporting quality. It also uncovers a situationin which auditors may be more likely to detect earningsmanagement. When auditors are led to accounts that con-tain errors, it appears that those errors raise a red flag,resulting in a greater likelihood of finding managed earn-ings in other parts of the financial statements. The studyalso points out an association between skepticism andearnings management detection. Even though professionalskepticism is practiced widely by auditors, the variabilityin skepticism displayed by auditors suggests that there isroom for further growth, through added practice andtraining.

This study introduces the concept of audit manage-ment, a broad topic that can be explored in future research,and one with important financial reporting implications.While studies have examined auditors’ responses to earn-ings management (e.g., Anderson, Kadous, & Koonce,2004; Nelson, Elliot, & Tarpley, 2002; Ng, 2007), auditorsin these studies were generally provided with evidence ofaccounting irregularities and asked to make a judgment.This study differs in that it examines the detection of earn-ings management rather than determining whether tobook an already identified audit difference.

This study also contributes to decision making researchthat investigates the effectiveness of diversion and distrac-tion techniques from both psychological and auditing per-spectives. On one hand, the psychological research ondiversion would predict that diverting an auditor to eitherclean accounts or accounts with other errors would be aneffective approach to hide earnings management. On theother hand, psychology and accounting research on infor-mation pursuit effects suggests that, while managers couldconceal earnings management with diversions to cleanareas, diversions to other errors would increase (ratherthan decrease) auditors’ detection of earnings manage-ment elsewhere in the financial statements. That is, if audi-tors discover errors in areas that they are diverted to, theyare more likely to react to the risk of misstatement in therest of the financial statements implied by these errors,and scrutinize the remainder of the financial statementsmore closely. Our results are consistent with these predic-tions. That is, the results indicate that diversions can be aneffective means of managing the audit when they do notpoint to errors. However, when these tactics point to othererrors, the discovery of those errors appears to heightenthe auditor’s search for earnings management, causingthe diversionary tactics to backfire. Our findings alsocontribute to the literature on information pursuit effects.Specifically, support for our hypothesis suggests thatdiverting auditors to search other accounts appears toamplify their reactions to what is (or to what is not) inthose other accounts.




Our study’s implications go beyond diversionary tacticsto conceal deliberate earnings management. While wefocus on the implications of our findings for audits whenmanagement deliberately conceals earnings manipula-tions, our experiment is more generally a test of diversions(whether deliberate or inadvertent) from accounts thatcontain a material overstatement of earnings (whetherintentional or unintentional) to accounts that do not. Forexample, managers could simply divert auditors awayfrom higher risk areas toward what they believe to belower risk areas, even if they are not explicitly managingearnings. Our findings suggest that such diversions couldalso significantly influence the auditors’ detection of mate-rial overstatements of earnings in this setting. Additionally,even when managers divert auditors toward cleanaccounts with good intentions, our result shows that thisdiversion still makes auditors less likely to detect a mate-rial overstatement of earnings elsewhere in the financialstatements. In these examples, this systematic judgmenterror may elevate auditors’ detection risk and damageaudit effectiveness, even when the conditions leading toit are unintentional.

As with all experimental research, we held constantmany important variables that auditors would experiencein practice. To compensate for this, we created an instru-ment of considerable depth to make the task more realistic,while still manipulating only the two factors. Futureresearch may investigate diversionary tactics in other auditcontexts to examine the generalizability of the effectsfound here. Future research can also examine other factorsthat influence the effectiveness of the diversionary tacticsinvestigated here, as well as examine the effect of differenttypes of diversions and other ways to manage the audit.

Acknowledgements

We would like to thank Chris Agoglia, Scott Asay, TimBauer, Efrim Boritz, Chris Chapman (editor), AndrewCohen, Fran Conlin, Kirsten Fanning, Steve Fuller, SteveGill, Steve Glover, Jeremy Griffin, Liang Guo, Lynn Hannan,Jackie Hammersley, Jessen Hobson, Jennifer Joe, KathrynKadous, Sandi Kim, Natalia Kotchetova, Tamara Lambert,Bob Libby, Greg McPhee, Drew Newman, Steve Perreault,Adam Presslee, Jom Ruangprapun, Steve Salterio, ChadStefaniak, James Wainberg, Alan Webb, Mark Zimbelman,two anonymous reviewers, and workshop participants atthe University of Massachusetts Amherst, Georgia StateUniversity, Miami University, the University of Waterloo,the BYU Accounting Research Symposium, the AAA AuditMidyear Meeting, and the AAA Annual Meeting for theircomments and suggestions.

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