Ethical Dilemmas in Auditing: Dishonesty or Unintentional ... - NHH

seduction theory suggesting that the root of the auditing problem appears to result in anunintentional bias rather than in dishonesty. Therefore, the SOX seems not to be the solution tothe auditing problem.From our point of view, previous research into auditors’ decision-making, instead ofaddressing their unintentional bias, has merely focused on isolating the causes provoking auditorambiguous reporting (Tucker et al., 2003; Joe, 2003). Furthermore, the way in which conflicts ofinterest interact in the auditor’s decision making has not been explored yet (Moore et al., 2006).We believe that the use of input-output models, rather than contribute to opening the so-calledblack-box of the auditing problem, has lead researchers to an incomplete understanding of themain true source of auditor’s biases.In this paper, we propose a modeling approach analyzing the interactions betweenauditors’ decisions and their perceptions of the potential consequences of their opinions. Inparticular, we build on previous research exploring the interactions into auditors’ cognitiveprocesses of three main ethical dilemmas (i.e., the self- fulfilling prophecy effect, the fear to besued, and the responsibility of providing warning signals), which auditors simultaneously facewhen evaluating the ability of their clients to continue in existence (Nogler, 2006). We believethat this innovative approach may provide a more complete picture of the auditing problem.To illustrate auditors’ involuntary bias we implement the Throughput model (TM), acognitive model to explain the role played by perceptions in human decision making (Rodgers1997, 2006), in connection with the moral seduction theory (Moore et al., 2006). On the onehand, the TM approach asserts that four major processing stages of perception (framing),available information, judgment (analysis), and decision choice and their interactions canprovide more meaningful relationships of the impediments or causes of decisions (Rodgers,4

1997). On the other hand, moral seduction theory suggests that structural features of the tightauditor-client relationship may cause auditors’ involuntary lack of independence. In this paperwe argue that three structural aspects (i.e., selective perception, escalation of commitment anddiscounting of information) of this particular relationship may lead auditors to avoid the issuanceof a warning signal (Bazerman et al., 1997; Bazerman et al., 2002; Moore et al., 2006).Eighty experienced auditors from two international accounting firms were simultaneouslyexposed to three auditing dilemmas when evaluating their clients’ ability to survive. UsingPartial Least Squares (PLS) method it was found empirical evidence of how those dilemmasaffect auditors’ opinions and, in turn, might lead financial statements users to inappropriatedecisions. Also, our results suggest that more than a regulation effort should be made inmonitoring those conflicts of interest in order to reduce unintentional bias.The paper is organized into six parts. Following the introduction, in the next section wepresent the usefulness of the TM in explaining auditors’ cognitive processes and the role playedby perceptions of ethical dilemmas. In the third section, we discuss previous research and theneed to develop the research hypotheses. The fourth part describes the hypothetical caseconducted. The fifth section presents the results obtained. The final section presents the principalconclusions and possible implications of the study.2. Using the TM to capture unintentional biasAuditing standards require an independent and objective evaluation of client’s ability tocontinue in existence in every audit (e.g., Statements on Auditing Standards (SAS) No. 59,International Standard on Auditing (ISA) No. 570). To that extent, auditors must developanalytical procedures to conclude their clients’ ability to survive. Considering that the major5

2012/6/3 Mentors & Research Topics12Name University Topic EconomyProf. Chao-MingFUProf. KuniyukiKitagawaChinese TaipeiNationalUniversityNagoyaUniversityIntroduction of neuronAnalytical ChemistryChineseTaipeiJapan3Prof. Ashwani K.GuptaUniv. ofMarylandDesign of a LiquidSeparating TrashcanUSA4Prof. Soo YongKIMKAISTMagnetic properties ofoxide filmsROK5Prof. In SeobHANUniversity ofUlsanIs there a gene for genius?ROK Mentoring Teams – 5 Teams, 21 MenteesMentor Mentees School EconomyProf. Chao-MingFUProf. KuniyukiKitagawaDong Hwan KIM Gyeongnam Science High School ROKJu Sang LEE Gyeonggi Science High School ROKBui Quang Tu Hanoi Amsterdam High School for Viet Namthe GiftedTuan Dao Manh Hanoi Amsterdam High School forthe GiftedViet NamYe Won KANG Changwon Science High School ROKHieu Le Tran Hanoi Amsterdam High School for Viet Namthe GiftedJap Xin YiSekolah Menengah Kebangsaan MalaysiaLutongGian Paolo B.SepulvedaPhilippines Science high School Philippines5

“P” in TM) may contribute to illustrate auditors’ reluctance to release warning signals toinvestors.Selective perception bias refers to auditors’ involuntary tendency to reach their own selfinteresteven when they try to be independent. Since auditors are hired and fired by their ownclients, they have persuasive economic arguments to support client-preferred goals (Bazerman etal., 2002; Kadous et al., 2003). Thus, in order to maintain expected audit fees, auditors may bereluctant to issue qualified audit reports. Escalation of commitment is defined as the tendency tocontinue to invest in the losing course of action, in the hope of a favorable turn of events (Keil etal., 2007; Wong et al., 2006). Assuming that after the issuance of a clean audit report auditorswill keep the client, they may be morally compromised to act in the same unethical way forsubsequent audits. Further, discounting of information bias refers to people tendency to be onlyaware of immediate consequences of their course of action. This bias may lead auditors toperceive potential reputation losses and lawsuit costs as distant and uncertain (Bazerman et al.,2002), favoring the issuance of clean audit reports.Following Moore et al. (2006)’s arguments, the impact of these conflict of interestsshould be interpreted as an involuntary bias rather than dishonesty. This unconscious bias inauditor reporting is depicted in the TM by (1) the direct arrow from perception to the judgmentstage, and (2) the indirect interaction between perception and information when the auditoremploys an analytical behavior. Finally, the TM also illustrates the possibility that the presenceof ethical dilemmas that may influence auditors degree of honesty, via a direct connection ofperception on decision. Thus, this potential unethical behavior could lead auditors to make adecision based on their self-interest (i.e., the fear to lose the client) by disregarding availableinformation (Rodgers and Gago, 2001, 2004, 2006; Rodgers et al., 2008b).8

H 1 : Auditors involuntarily fearing the self-fulfilling prophecy effect will show a lowerlikelihood of issuing a going concern opinion.3.2. Auditors’ perception of the litigation risk exposureThe perception of the self-fulfilling prophecy effect is not the only conflict of interests,which may affect auditors reporting behavior. A Type II misclassification occurs when a cliententers bankruptcy but did not receive a prior warning signal from the auditor in the periodimmediately preceding the bankruptcy filing (Geiger et al., 2005). Many research papers havefound that the frequency of error Type II is very high, considering that auditors only issuewarning signals for less than half of failing firms (Geiger et al., 2005; Nogler, 2006). In the caseof the Type II misclassification, auditors’ primary risks are lawsuits from stakeholders, chargesof auditor negligence and loss of reputation (Nogler, 2006). On the one hand, empirical researchhas found some incentives to avoid Type II misclassification. LaSalle et al. (1996) suggest thatmost auditors are sensitive to litigations and believe that the issuance of a going concern opinionwould offer some protection in terms of liabilities. For example, Joe (2003) indicated thatnegative events in the press influenced auditors’ perception of a client’s bankruptcy probability,increasing auditors’ propensity to modify the audit report. Mong and Roebuck (2005) conductedan experiment with practitioners in corporate liquidation and advanced law students to analyzewhether the issuance of a warning signal has any effect on auditor litigation risk. The results ofthis experiment suggest that the qualified audit report acts as a red flag and reduces potentiallitigants’ likelihood of pursuing litigation against the auditor.However, the assumption that auditors are highly sensitive to Type II misclassificationseems to be far away from reality given the recent reductions in the threat of legal penalties andauditors’ tendency to underestimate potential reputation losses and lawsuit costs (Moore et al.,11

2006; Bazerman et al., 2002; Rodgers et al., 2008b). For instance, Geiger et al. (2006) concludethat the Private Securities Litigation Reform Act of 1995 has significantly reduced the litigationexposure of US audit firms in the evaluation of clients’ ability to continue in existence. In othercontexts, such as the cases of Europe and Asia, the studies of Ruiz-Barbadillo et al. (2004),Vanstraelen (2002) and Lam and Mensah (2006) have found low litigation risk in auditenvironments like Spain, Belgium and Hong Kong. These arguments lead us to our secondhypothesis based on the discounting of information bias:H 2 : Auditors involuntarily perceiving litigation risk as uncertain will show a lowerlikelihood of issuing a warning signal to investors.3.3. Auditors’ perception of their whistle-blowing functionA third dilemma faced by auditors is related to their responsibility to provide earlywarning signals. Following Ruiz-Barbadillo et al. (2005: 598), this dilemma considers clients’ability to survive is to provide users of financial statements with an early red flag of potentialfinancial problems. In this regard, an early warning signal in the audit report should be extremelyimportant for financial users since this warning may affect investors and other third parties’decisions in terms of re-allocation of credit (Guiral-Contreras et al., 2007). Furthermore, auditorsare expected to exercise professional skepticism in conducting an audit, applying an appropriatemeasure of suspicion to reduce harmful consequences for third parties. Thus, even a qualifiedaudit report may provoke a self-fulfilling prophecy effect, auditors’ may perceive that the publicis expecting from them a whistle-blowing function.However, auditors viewed as a public watchdog has been the subject of intense debateamong academics and professionals (Venuti, 2004). Some authors claim that this responsibilitybecomes too close to a financial qualification of the client, which would nullify the main purpose12

of auditing, that is, the verification of the reliability of the client’s financial information(Bellovary et al., 2006). In contrast, other authors consider that the auditor’s evaluationregarding the client’s ability to survive is very important because it is extremely valuable inreducing uncertainty for statement users (Hopwood et al., 1994). Independent auditors must haveaccess to information not publicly available relating to the activity and future plans of theirclients. Consequently, since they have both specialist knowledge and access to insideinformation, they are in a better position than statement users to predict the resolution ofuncertainties. Assuming that auditors would be able to avoid conflicts of interest in order toprovide early warning signals to investors, we propose our third hypothesis:H 3 : Auditors perceiving that their opinions are critical for financial statements users willshow a higher likelihood of issuing a warning signal.Finally, we also considered the possibility that the aforementioned conflicts of interestmight lead auditors to an intentional bias behavior by disregarding available evidence anddeciding to act in their self-interest (Rodgers and Gago, 2001, 2006). To this end we propose ourfinal hypothesis:H 4 : Conflicts of interest, such as the self-fulfilling prophecy, low litigation risk and theresponsibility of providing warning signals, provoke auditors’ dishonesty by disregardingthe development of an analytical behavior required by auditing standards.4. Research Design4.1. ParticipantsEighty partners and audit managers from two Spanish international accounting firms (34and 46 from each firm, respectively) participated in this study. The subjects’ average age was 3513

years, with average experience of 15.7 years 1 . The responses were captured during differenttraining sessions in each of the two accounting firms. The time spent for completion ranged from50 to 60 minutes.4.2. TaskSubjects were informed of the importance of working independently. We also guaranteedthat their responses would remain anonymous. Each participant judged the financial status of afictitious company with severe financial distress (i.e., negative retained earnings, negativeworking capital, poor liquidity and high financial leverage). After receiving backgroundinformation about a hypothetical client’s financial position 2 , auditors were provided with fourpieces of evidence (see Appendix).First, they received two items of negative financial evidence. Then, they processedanother two items of positive financial information. After each item they were requested toestimate the company’s ability to continue in existence in probabilistic terms 3 . Finally, they wereasked to answer whether they would qualify the audit report and indicate what type of opinionthey would issue. Further, at the end of the questionnaire each participant was asked to identifyeach item as negative or positive evidence as well as an evaluation of its relevance 4 .We also introduced into our study auditors’ perceptions as a proxy for potential conflictsof interest regarding the consequences of their opinions. Each auditor was asked to assess threequestions related to the auditing conflicts of interest described in a previous section of thispaper 5 . To capture auditors’ perception of the dilemma related to the self-fulfilling prophecyeffect we asked auditors to respond to the following question: “Do you believe that the issuanceof a warning signal precipitates the bankruptcy of the client?”. The second question was relatedto auditors’ perception of the litigation risk exposure dilemma and was measured by asking14

auditors the following question: “Do you believe that the issuance of a warning signal acts as ashield against potential litigations (for a finally bankrupt client)?”. Finally, to measure auditors’perception of their whistle-blowing function, that is, the responsibility to provide early warningsignals to financial statement users, auditors answered the following question: “Do you believethat an on-time release of a red flag is essential for financial statement users’ decision making?”To avoid possible carry-over effects between perceptions and decisions, we manipulatedthe order in which these questions were provided to subjects. Approximately, half of the auditors(n = 44) were asked about their perceptions on auditing dilemmas at the beginning of thequestionnaire, that is, before receiving the background information. The other half (n = 36)answered the questions after they completed their decision choice. In this regard, a Kruskal-Wallis test did not find statistically significant differences in perception responses among the twogroups (χ² = .667; p = .498).5. ResultsWe used Partial Least Squares (PLS) to analyze our research model depicted in Figure 2.PLS is a latent structural equation modeling technique that allows the researcher to test therelationship within the measures (the measurement model) and the hypothesized relationshipamong the measures (the structural model) simultaneously (Lohmoller, 1988; Wold, 1983). It isparticularly useful for studying structural models involving multiple latent constructs withmultiple indicators.There are three main advantages of PLS. First, unlike other structural equation models(LISREL, AMOS, EQS, etc.), the assumptions underlying PLS are less stringent (Fornell andBookstein, 1982; Lohmoller, 1988; Chin et al., 2003). PLS imposes minimal restrictions on15

measurement scales, sample size, and residual distributions. PLS has superior ability to handlecausal-predictive analysis in situations of high complexity but low theoretical underpinnings.PLS uses a principal-component model in which no random error variance or measure-specificvariance (i.e., unique variance) is assumed. Parameters are estimated in a manner that maximizesthe amount of explained variance in a set of observed measures. Goodness of fit is evaluated onthe basis of the percentage of variance explained in the specific regression. Because the PLSapproach estimates the latent variables as exact linear combinations of the observed measures, itoffers the advantage of exact definition of component scores.In PLS, loadings of measures of each construct can be interpreted as loadings in aprincipal components factor analysis. Paths between latent constructs can be interpreted asstandardized beta weights in a regression analysis. Because PLS does not make distributionalassumptions, traditional parametric procedures of significance testing are not appropriate. Thus,we use bootstrapping re-sampling procedures to estimate factor loadings and path coefficients inthe model (Chin, 1998). Table 1 summarizes descriptive statistics for indicators.(Insert Table 1 about here)5.1. Measurement of constructsThe latent variables (constructs) of our model were captured using single and multi-itemsvariables (measures) for information processing, judgment, decision and perception stages (seetable 2). In our TM model the information stage consists of two constructs: negative and positiveevidence. The negative evidence construct was measured by two indicators, that is, auditors’evaluation of the relevance of the first two pieces of negative information (N1 and N2). While,auditors’ assessment of the two mitigating items (P1 and P2) was used to capture the positiveevidence construct.16

(Insert Figure 2 about here)Three latent constructs formed the perception stage: self-fulfilling prophecy, litigationrisk exposure and the whistle-blowing function. Each of these constructs were measured by asingle indicator (C1, C2 and C3, respectively). We used four indicators (JN1, JN2, JP1, JP2), i.e.,auditors’ estimation of the client’s ability to survive after each item of evidence, to capture thejudgment latent construct. Finally, the decision stage was measured by another two indicators:whether the auditor would qualify the audit report (D1) and the type of audit report he/she wouldissue (D2).5.3. Measurement validationOur model was estimated using the computer program PLS-Graph version 3.00. Themeasurement model evaluates the relationship between indicators and latent constructs byassessing the reliability and validity of the scales measures. Each measure’s reliability is assessedby examining the loading of the indicators on the corresponding construct. All measures have aloading level above 0.70 (see Table 2). In addition, measurement residuals are small. Allloadings have the expected signs (i.e., non-negative) and are statically significant at the 0.001level (one-tailed). Further, all constructs present a composite reliability (Fornell and Larcker,1981) above 0.70, the benchmark level suggested by Nunnally (1978).(Insert Table 2 and 3 about here)Also, convergent and discriminant validity can be evaluated within the PLS model.According to Chin (1998), convergent and discriminant validity is inferred when the PLSindicators (1) load much higher on their hypothesized factor than on other factors (i.e. ownloadingsare higher than cross-loadings), and (2) when the square root of each construct’sAverage Variance Extracted (AVE) is larger than its correlations with other constructs (the17

average variance shared between the construct and its indicators is larger than the varianceshared between the construct and other constructs).To test the first condition, the CFA procedure in PLS was performed. That is, anexamination of measure loadings on constructs correlations. The correlation matrix in Table 3shows that no indicator loads more highly on another construct than it does on the latentconstruct it is intended to measure. Convergent validity of a construct is measured by the ratio ofthe variance of its indicators captured by the construct to the total amount of variance (averagevariance extracted, ovc ). The total amount of variance includes the variance due to measurementerror. As a rule of thumb, a ratio of less than 0.50 implies the convergent validity assumption isviolated because more variance is explained by the error than the construct. In our model,average variance extracted (ovc) ranges between 0.63 and 1.00, indicating satisfactoryconvergent validity for the constructs.(Insert Table 4 about here)Satisfactory discriminant validity among constructs is obtained when the squaredcorrelation between any two constructs is statistically less than the ovc . This implies that thevariance shared between any two constructs is less than the variance shared between a constructand its indicators. The low and moderate average squared correlations among constructs in Table4 show that our model also satisfies the condition for discriminant validity. Thus, it can beconcluded that the constructs are measured with sufficient precision, that is, the model is bothreliable and valid 6 .5.4. The structural model18

Our cognitive model predicts that auditors’ evaluation begins with a mental integration ofthe financial information items and its possible interaction with auditors’ perceptions, whichleads to the judgment stage (i.e., client’s ability to continue in existence). On a final stage, bothjudgment and perception lead to the final audit report decision.Apparently, table 1 shows that auditors gave more relevance to those items that mightarose doubts about client’s ability to survive (negative evidence) in comparison with thosesupporting client’s viability (positive evidence). On an 11-point scale, the mean ratings were7.86 for negative items and 6.66 for positive information. Further, a t-test comparison indicatedsignificant differences between both types of evidence (t = 4.29; p = .00). Thus, we mightconclude that auditors exercised an appropriate measure of suspicion or professional skepticismbased on the analytical behavior required by auditing standards. However, a higher ranking ofthe negative evidence (individually considered) does not necessarily mean that auditors wereskeptical.PLS simultaneous analysis allows us to interpret how auditors integrated the financialevidence (positive and negative) in their causal reasoning, which may also be driven by theirperceptions of conflicts of interest. Overall, the results seem to be consistent with the assumptionthat auditors simultaneously faced ethical dilemmas when evaluating the financial status of aclient with clear symptoms of financial distress 7 . On the one hand, Figure 2 shows that auditorsdid not employ an analytical and objective pathway as required by auditing standards (i.e., I →J→D). Negative evidence was highly correlated with litigation risk exposure (r 3 = .304, p

Similarly, those auditors perceiving that a going concern opinion is useful for financialstatements users’ decisions gave also more relevance to the negative financial information 8 .However, negative financial evidence did not have a direct impact on the judgment stage. Inaddition, the non-significant correlation between the perceived consequences of the self-fulfillingprophecy effect and the negative evidence constructs seems to support the argument that the fearto precipitate the bankruptcy of the client led auditors to underestimate this type of evidence.Further, although positive evidence was not correlated with any auditors’ perception of theaforementioned ethical dilemmas and it had a direct impact on the judgment stage (β 2 = .510, p

would be critical for users’ decisions were more severe in calculating the client’s probability ofbankruptcy. Finally, we analyze the impact of judgment and perception stages on the final auditreporting choice. PLS shows that only the judgment construct was significant in explainingauditors’ decisions (β 9 = .286, p

audit dilemmas were examined in our study: the self- fulfilling prophecy effect, the fear to besued, and the responsibility of providing early warning signals.We implemented the TM model in connection with moral seduction theory enabling us toexamine the impact of perceived consequences on auditors’ decision making processes. We havebeen able to reflect on three principal questions. First, only positive evidence had a significantimpact on the judgment stage. That is, to the detriment of the negative evidence, auditors onlygave relevance to that information which might mitigate doubts about client’s ability to continuein existence. Second, in spite of the significant correlation between both the litigation exposureand the whistle-blowing function dilemmas and the negative evidence, auditors based theirjudgment only on the positive information. Finally, the third major point that we put forwardrefers to the delicate ethical conflict of interests faced by auditors at the judgment stage, wheresimultaneously weighed the potential effect of the self-fulfilling prophecy versus theirresponsibility of providing an early warning signal to investors.We believe that this paper contributes to a better understanding of the very inherentcomplexity of auditors’ evaluation of their clients’ ability to survive. In other words, thisresearch paper may aid regulators, legislators in the restoring of public trust in auditors’functions by improving its knowledge and the interaction of conflicts of interest. In this regard,our results support that the current reforms regarding auditors’ independence (e.g., SOX) areinsufficient to avoid auditors’ unintentional reporting bias. Therefore, further research regardingauditors’ unintentional bias is encouraged.Our paper is subject to possible limitations. First, our research focuses on three mainincentives which may affect the auditors’ decision making process. Research can be expanded by22

analyzing other potential incentives auditors may have to favor client-preferred outcomes, suchas, economic dependence (i.e., audit and non-audit fees, auditor changes, etc.) and reputationloss. Second, we analyzed a hypothetical scenario without manipulation checks of our study’svariables. Rather than trying to manipulate auditors’ perceptions through different manipulatedscenarios, we used their direct responses to three ethical dilemmas. Finally, further research mayalso explore the role played by auditors’ expertise as a moderator variable of auditors’unintentional biases.Notes1 A Kruskal-Wallis test did not find statistically significant differences in experience amongsubjects of the two audit firms (χ² = .737; p = .391).2 The case materials included the balance sheet, the income and financial ratios on December31st 2005, together with the comparative data corresponding to the financial year 2004.3 Subjects used a scale of 0-100, where zero was “The Company will not be able to continue inexistence” and 100 was “The Company will definitely able to continue in existence”.4 A Likert scale of 11 points was employed, where 1 = totally irrelevant, and 11 = very relevant.5 Another Likert scale was employed, where 1 = I strongly disagree, and 11 = I strongly agree.6 Another meaningful indicator of the fit of the model with respect to its measurement is theoverall communality coefficient (in our model 0.76). This exceeds Falk’s (1987)recommendation that this coefficient should be greater than 0.30.7 PLS path coefficients are shown in Figure 2.8 These results seem to support the TM argument that information and perception may beinterdependent in the decision making process.ReferencesBazerman, M. H., Loewenstein, G. and Moore D. A. (2002) ‘Why good accountants do badaudits’, Harvard Business Review, 80, pp. 96-103.23

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TABLE 1Descriptive StatisticsIndicator Mean Standard DeviationNegative information 1 (N1)Negative information 2 (N2)Positive Information 1 (P1)Positive Information 2 (P2)Viability after N1 (JN1)Viability after N2 (JN2)Viability after P1 (JP1)Viability after P2 (JP2)Unqualify vs. Qualify (D1)Type of Audit Opinion (D2)Self-fulfilling prophecy (C1)Litigation risk (C2)Whistle-blowing (C3)7.478.257.016.3335.6528.3747.1851.811.061.215.686.018.221.811.792.152.3121.1619.4321.8023.80.243.5442.412.341.5429

TABLE 2Measurement Model Parameter EstimatesConstructs and Indicators Loadings T-StatisticNegative Evidence(Composite Reliability = 0.722)ConvergentValidity (ovc)DiscriminantValidity *0.631 0.06Negative information 1 (N1)Negative information 2 (N2)0.7120.8672.302.73Positive Evidence(Composite Reliability = 0.896)0.811 0.06Positive information 1 (P1)Positive information 2 (P2)0.9460.85251.1713.92Ability to Continue(Composite Reliability = 0.888)0.666 0.06Viability after N1 (JN1)Viability after N2 (JN2)Viability after P1 (JP1)Viability after P2 (JP2)0.8160.8150.8440.78712.2113.2516.1113.20Self-fulfilling prophecy perception(Composite Reliability = 1.000)1.000 0.01Self-fulfilling prophecy (C1) 1.000 0.00Litigation risk exposure perception(Composite Reliability = 1.000)1.000 0.03Litigation risk (C2) 1.000 0.00Whistle-blowing perception(Composite Reliability = 1.000)1.000 0.06Whistle-blowing (C3) 1.000 0.00Audit Report(Composite Reliability = .962).714 0.04Unqualify vs. Qualify (D1)Type of Audit Opinion (D2).972. 9516.9420.31.926* The entry in each row is the average of the squared correlations of the particular construct with all other constructs.30

TABLE 3Correlation Matrix between Latent Variables Scores and IndicatorsNegativeEvidencePositiveEvidenceAbility tocontinueSelf-fulfillingPerceptionLitigationrisk exposurePerceptionWhistleblowingperceptionAuditReportNegative information 1 (N2) .713** 0.184 -0.088 0.120 .354** .397** -0.160Negative information 2 (N2) .868** 0.014 -0.124 -0.001 0.166 .272** -.287**Positive information 1 (P1) 0.145 .947** .499** 0.130 0.079 0.129 0.133Positive information 2 (P2) 0.016 .852** .307** -0.013 0.112 0.177 0.214Viability after N1 (JN1) -0.108 .326** .844** 0.127 0.146** -0.287** 0.327*Viability after N2 (JN2) -0.198 .387** .787** 0.157 -0.165 -0.206 0.197Viability after P1 (JP1) -0.005 .387** .815** 0.197 -0.049 -.170* 0.210Viability after P2 (JP2) -0.126 .431** .816** 0.203 -0.048 -0.110 0.222Self-fulfilling prophecy perception (C1) 0.062 0.087 0.209 1.000** -0.147 -0.042 -0.061Litigation risk exposure perception (C2) .304** 0.101 -0.098 -0.142 1.000** .295** -0.060Whistle-blowing perception (C3) .403** 0.162 -.239* -0.042 .295** 1.000** -0.103Unqualified vs. Qualified (D1) -.289** .186 .311** 0.086 -0.114 -0.124 0.972**Type of Audit Opinion (D2) -0.270* 0.158 0.248* -0.023 0.015 -0.067 0.952*** Significant at p

TABLE 4Correlation Matrix between Latent Variables Scores and Square root of AVE (diagonal elements)NegativeEvidencePositiveEvidenceAbility tocontinueSelf-fulfillingPerceptionLitigation riskexposurePerceptionWhistleblowingperceptionAuditReportNegative Evidence 0.794 - - - - - -Positive Evidence 0.106 0.900 - - - - -Ability to continue -0.136 0.468 0.815 - - - -Self-fulfillingPerceptionLitigation risk exposurePerception0.062 0.087 0.209 1.000 - - -0.304 0.101 -0.098 -0.147 1.000 - -Whistle-blowing perception 0.403 0.162 -0.239 -0.042 0.295 1.000 -AuditReport-0.292 0.180 0.295 0.061 -0.060 -0.103 0.844

FIGURE 1TM Decision Making Model in the Going Concern EvaluationMoral seduction theoryPunintentionalunintentionalintentionalI J Danalytical pathwaywhere P = auditors’ perception of ethical dilemmas (self-fulfilling effect, litigation risk exposure andwhistle-blowing function), I = information (negative and positive information), J = judgment (abilityof the client to continue in existence), and D = decision choice (type of audit report)

FIGURE 2PLS resultsPerceptionC1 C2 C3Whistle-BlowingFunctionSelf-FulfillingEffectLitigationRiskExposureInformation1 r 1 = .062r 2 =.087r 3 =.304 ***r 5 =.403 ***β8=. 027β3=.155**N1N2NegativeEvidenceβ1= -. 082r 4 =.101r6 =.162β4= -.025β5= -. 276***β6= -. 003β7= -. 074β2=.510 ***JudgmentAbility toContinueR 2 = .379β9=.286***DecisionAuditReportR 2 = .120P1PositiveEvidenceP2JN1 JN2 JP1 JP2Probability of financial viability(non-bankruptcy) after each piece of evidenceQualifiedvs.Unqualified(D1)OpinionType (D2):-Adverse-Modified-Clean*Significant at p

APPENDIXITEMS OF EVIDENCE PRESENTED TO AUDITORSFirst Item“The management and representatives from the trade union of Drinks Inc. state that there exists thepossibility of persistent strikes in early 2006 as a result of the employment regulation plan. Moreover,it is speculated that the company will be immersed in a raft of serious lawsuits for alleged unfairdismissal of highly qualified staff” (negative evidence, N1).Second Item“In March 2006, Drinks Inc.’s bank announced to the management that the renewal of the company’sline of credit will be denied. After negotiations with other financial institutions, there is evidence thatthe new line of credit will provoke a significant increase of the interest rate. For the period ended in2006 is expected an interest expenses in the amount of 52.000 thousand of euros” (negative evidence,N2).Third Item“After examining management’s plans, there is evidence supporting: (1) the possibility of receiving anon-refundable government grant to an estimated value of 70.000 thousands of euros; (2) a beerconsumption is expected to grow by 2%-3 % annually, and (3) a reduction in operating costs of 15%without affecting current productive capacity” (positive evidence, P1).Fourth Item“The Company possesses a recreational land property ready for sale. That sale would not be criticalto the company’s normal operating activities. These plots have been valued in 60.000 thousands ofeuros” (positive evidence, P2).