Detecting Framing Effects in Financial Statements

Detecting Framing Effectsio Financial Statements*

KARIM JAMAL University of Alberta

PAUL E. JOHNSON University of Minnesota

R. GLEN BERRYMAN University of Minnesota

Abstract, in this study, we address the question of what kinds of cognitive representa-tions auditors use in a situation of potential financial statement fraud. We divide the prob-lem of detecting fraud into two parts: detecting the frame management has constructed tomask the fraud, and detecting the fraud. We examine two ways proposed by Kahnemanand Tversky (1986) for detecting a frame: (I) use of multiple representations that providealternative interpretations of data in the financial statements: and (2) use of a procedurethat transforms financial statement data into a standard representation.

Twenty-four audit partners served as participants in the study. Each partner conducteda simulation of a concurring partner review. AH auditors reviewed four cases in whichmanagement had created a misleading description of the company (a frame) and a finan-cial statement fraud.

The results support Kahneman and Tversky's proposal that frames can be detected bytransforming a problem into a standard representation. Auditors who used a standard rep-resentation successfully detected management's frame, aggregated the items, and detect-ed fraud in all four cases. Auditors who used a standard representation followed a proce-dure specified by generally accepted auditing standards (Canadian Institute of CharteredAccountarits 1988, American Institute of Certified Public Accountants 1984) for aggre-gating items. Auditors who used multiple representations detected management's frameon all four cases. These auditors, however, did not use the aggregation procedure speci-fied by auditing standards and failed to detect the fraud on all four cases.

The term frame has been used to refer to those features of a task description thatcan be aliered to change how it is perceived and acted upon (e.g., Kahneman andTversky 1986; McNeil, Pauker. Sox, and Tversky 1982). A framing effect issale to occur when a change in the description of a task (i.e., a frame), whichdoes not alter its normative meaning, changes the decision that is made. Framingeffects have been studied with a variety of experimental tasks such as describingthe sffccis of a vaccination program in terms of "lives saved" or "lives lost"(McNeii et at. 1982), labeling the differences between credit card prices andcash prices as "discounts" or "surcharges" (Thaler 1980), and describing a

* Accepted by Michael Gibbsns. We wish to thank ail the auditors who participated in the study.Wi'.houl the generous contribuiion cf their time, the work could not have been done. We also'.hank the SEC Financial Reponing Institute, administered by the School of Accounting at theUnj%'ersity of Southern California, foi' their generous financial support. We gratefully acknow-ledge comments from Stefario Gra?:io!i, John Hand, Linda McDaniel, James Peters, StevenSaSierio, participants at the §994 CAR Conference, workshop participants at the University ofWai:er.?oo, ami two anonymous referees.

Ccwempomry.Accounting Research Vol.12 No.l (Fail 1993) pp. 85-105 ©CAAA

86 Contemporary Accounting Research

variety of gambles as "gains" or "losses" (Kahneman and Tversky 1979).Although the primary focus of the literature has been on demonstrating the exis-tence of framing effects in experimental settings, Kahneman and Tversky (1986)proposed framing as a general cognitive phenomenon and suggested that deci-sions made by various individuals may be influenced by the way in which infor-mation is presented and interpreted.

In financial reporting, managers are given discretion to frame their annualreport in order to communicate their private infonnation to investors and otherusers of financial statements (Healey and Palepu 1993). However, managers canalso frame their report to mislead users of financial statements or to maskimproper actions such as fraud (Johnson, Jamal, and Berryman 1991). To guardagainst the possibility of improper management actions, auditing standardsrequire the auditor to maintain an attitude of professional skepticism (CanadianInstitute of Chartered Accountants [CICA] 1991a; American Institute ofCertified Public Accountants [AICPA] 1988a) and to warn the auditor to be alertto evidence of material misstatements and indications of a lack of managementintegrity (CICA 1991a, AICPA 1988a). An auditor may maintain an attitude ofprofessional skepticism by constructing cognitive representations that overridethe misleading iTame(s) constructed by management.

The study presented here focuses on how auditors may detect financialstatement fraud. Fraud detection involves two steps: (1) detecting the frame(s)constructed by management to mislead the users of financial statements; and (2)detecting the fraud. The knowledge used by auditors to overcome management'sframes and to detect fraud is identified by analysis of thinking-aloud comments(protocols) and conclusions reached by 24 audit partners who analyzed thefinancial statements of four companies while performing a concurring partnerreview.

BackgroundOne way to interpret framing effects is through the concept of a representation.Human agents generally perform tasks by constructing an internal representationof the task situation (Newell and Simon 1973; Gibbins and Jamal 1993). A rep-resentation in this sense is a mental image of task-relevant concepts such asaudit risk and materiality, and their relationships with one another and with taskinformation (Chi, Feltovich, and Glaser 1981). A framing effect occurs whenalternative descriptions (frames) of a problem activate different representationsin the mind of a problem-solving agent.

Kahneman and Tversky (1986) propose two ways in which the targets of afirame can detect an attempt to manipulate their behavior: (1) consider alterna-tive representations of the given problem; and (2) adopt a procedure that willtransform all problems into a standard representation. Kahneman and Tverskyargue that the use of multiple representations leads to the observation by an indi-vidual agent that different representations of the same situation (e.g., gains vs.losses) have different solutions. Having realized that any given representationmay not be valid, the individual can then choose among the set of possibilities.

Detecting Framing Effects in Financial Statements 87

The framing literature does not indicate how an individual shotild make thischoice. Auditors who consider multiple representations of a problem may be lesslikely to be overconfident in their judgments (Koonce 1992), and less likely toterminate prematurely their information search processes (Bedard and Biggs1991).

The use of a standard representation (e.g., use of expected value to evaluategambles) results in the same decision for different versions of the same problem.Procedures for converting problems into a standard representation are some-times taught in business schools (e.g., convert different streams of cash flowsinto net present value), although, for the most part, these procedures aredesigned to solve well-structured problems, rather than the kind of ill-structuredproblems encountered in most real-world settings. Kahneman and Tversky(1986) suggest that experienced individuals may develop a standard representa-tion as an effective and efficient means of solving ill-structured problems.

In the laboratory tasks used in the framing literature, both the use of multi-ple representations (gains and losses) and the use of a standard representation(expected value) lead to the normative solution to a problem. Very little addi-tional problem solving is required once a representation is activated. In complexdomains such as auditing it is unlikely that problem solving can be accom-plished by simply activating a representation. Large amounts of data must beprocessed and interpreted before a solution can be reached. The processing andinterpretation of data raise an additional issue about how items should be aggre-gated. Auditing standards require the use of a complex aggregation procedurewhere misstatements are first aggregated by account balance or class of transac-tioji, then aggregated by intermediate categories such as current assets, andfinally the highest level of aggregation is based on net income or net assets(CICA 1988, AICPA 1984).

Detecting Management FraudAn auditor is required by auditing standards to be skeptical (CICA 1991a,AICPA 1988a) with regard to management's assertions. The auditor must becareful, however, to distinguish among the legitimate exercise of managementjudgment, unintentional errors in judgment, and improper actions that constitutefraud. Detecting actions by management to present (to frame) a favorable reportof the company is not sufficient to conclude that the financial statements arefraudulent. Such a judgment is made only if management's actions can be deter-mined to be intentionally in violation of generally accepted accounting princi-ples (GAAP). The auditor has to thus generate an expectation, identify data thatare inconsistent with the expectation, interpret the effects of the inconsistency interms of its compliance with GAAP, and conclude that management intentional-ly violated GAAP.

A study by Johnson et al. (1991) suggests that a manager can use three tacticsto deceive an auditor. One tactic is to create a misleading description (such as pre-senting a declining company as a growth company) in order to induce the auditorto generate incorrect expectations and thus to fail to identify inconsistencies. ITie


second tactic is to create a frame that induces the activation of nonirregularityhypotheses to evaluate inconsistencies that are detected. The third tactic is toavoid the appearance of impropriety by making a series of small (individually im-material) manipulations to specific accounts in the financial statements and con-structing rationalizations for the resulting account balances. Under all threetactics, management may succeed if the auditor uses a single representation tointerpret each inconsistency. An auditor who uses a single representation will pro-pose a different (single) hypothesis to interpret each inconsistency detected, useeach hypothesis as a unit of analysis, and aggregate inconsistencies within eachhypothesis.

Kahneman and Tversky (1986) suggest that one method for detectingIrames is to propose multiple hypotheses to interpret each inconsistency detectedin the financial statements. An auditor who uses this strategy must assess thelikelihood of two or more hypotheses that are proposed for each inconsistency.The auditor should then use the aggregation procedure specified by auditingstandards (CICA 1988, AICPA 1984) to evaluate the detected inconsistencies.An alternative approach suggested by Kahneman and Tversky (1986) for detect-ing frames is to convert the data into a standard representation. In auditing, astandard representation consists of a single hypothesis and a common unit ofanalysis such as effect on net income.

Under generally accepted auditing standards, choice of an audit opinionunder these circumstances depends on an assessment of management's inten-tions, the degree of materiality, and/or the pervasiveness of the departure fromGAAP. A qualified opinion for noncompliance with GAAP can be issued whenthe auditor believes that the overall financial statements and the auditor's reportresult in a fair presentation. An adverse opinion must be issued if the auditorbelieves that management intentionally violated GAAP, that the condition beingreported upon is extremely material, and/or that the departures from GAAP areso pervasive as to make the financial statements misleading (CICA 1991b,AICPA 1988b).

The present paper has two objectives. The first objective is to understandthe content of multiple representations in auditing and how they influence theprocess of generating expectations, interpreting inconsistencies, and detectingframe(s) constructed by management. We seek to understand how the effects ofinconsistencies are aggregated by auditors who use multiple representations, andwhether detection of management's frame also leads to detection of fraud. Thesecond objective is to understand the nature and effectiveness of the use of astandard representation in auditing and how it influences the process of inter-preting inconsistencies and detecting fraud.

Method

TaskIn the Johnson et al. (1991) study, a case called Surgical Products was construct-ed from annual and lOK reports of a medical products manufacturing company


whose management had perpetrated a financial statement fraud. The case con-tained a description of the company's business; its products, markets, competi-tion, labor relations., and management, as well as a full set of financial state-ments and related notes. The report prepared by management described the com-pany as a financially successful, high-technology company in the medical prod-ucts industry. This description constituted a frame that was designed to elicit agrowth-company representation in the mind of auditors who reviewed the finan-cial statements. A growth-company representation for Surgical Products is inap-propriate. The company is actually in decline and only appears to be growingdoe to a misleading descdption (frame) and the fraudulent financial statementsprepared by management.

Io the present investigation the Surgical Products case from the Johnson etal. (199!) study was modified in two ways. (1) The industry in which the fraudoccurred (medical products) was changed to an industry in which frauds are lesslikely to occur (paper products). The industry manipulation was made to influ-ence the difficulty of detecting management's frame. A low-risk industry shouldgive auditors comfort and thus raise their tolerance for deviations from expecta-tions. (2) The strength of cues (materiality) manipulated by management to cre-ate the fraud was reduced in order to investigate how the aggregation of theeffects of inconsistencies is carried out and what effect it has on fraud detection.

In the Surgical Products case, management had overstated reported netincome by 90 percent. This was done by overstating the cost of inventories,improperly recording revenue on items shipped to distributors that were subjectto return, improperly capitalizing engineering labor costs as part of molds andd;es, and improperly capitalizing research and development costs. A weakversion of this medical case was constructed by reducing the dollar amount ofmaaipulatjon in these cues such that income was cumulatively overstated by50 oercent.

A new case was developed in the paper products industry using a lOK reporthorii an actual paper products company. The Surgical Products fraud was insert-sd into that paper company case by creating the same magnitude of misstate-iTPents to cost of inventory, recording of revenue, improper capitalization of engi-neering iabor costs, and improper capitalization of research and developmentcos-s. A weak version of the paper company case was constructed by reducingihe dollar amount of cues inanipulated in the same way as that used to create theweak-cue medical case. The manipulation of the original Surgical Products casercsirlted m four cases (a strong- and weak-cue case in the medical products indus-iry and a strong- and weak-cue case in the paper products industry).

Some inconsistencies, such as capitalization of research and developmentcQiis, are in violation of GAAP, which explicitly requires these costs to beexpenssd Other inconsistencies (e.g., inventory, revenue recognition) indicatepnt!.=ntial overstatements that are not unambiguously misstated. Other actions bymanagement, such as a change in accounting estimate and capitalization of inter-eEt are aljowed by GAAP but signal an aggressive approach to manage thereported accounting numbers. Results from a study of a concurring partner


review that used the U.S. Surgical Case (Johnson et al. 1991) indicate that audi-tors can infer a pattern of aggressive actions by management, become skepticalabout management's assertions (CICA 1991a, AICPA 1988a), and possibly infermanagement's intent to mislead users of the financial statements.

ParticipantsThe four cases were given to 24 practising auditors who were partners in nineU.S. national public accounting firms. The auditors were classified into one oftwo experience levels. Experienced audit partners were defined as auditors whohad more than 15 years of audit experience, including five or more years experi-ence as audit partners. New audit partners were defined as auditors who hadachieved partnership status within the last two years. These individuals had nineto 13 years of audit experience. Six experienced audit partners had specialized ineach of three industries: the medical products industry, the paper products indus-try, and general manufacturing. Six new audit partners had specialized in themedical products industry.'

ProcedureEach auditor was given each of the four cases in a fixed (conceptual) order andasked to perform the task of concurring partner review while thinking aloud oneach case. All auditors solved the weak-cue case in the unfamiliar industry first,followed by a weak-cue case in the industry in which they had specialized.^ In asecond session held after a two-week interval, these auditors solved a strong-cuecase in the unfamiliar industry followed by a strong-cue case in the industry inwhich they had specialized.

Ordinarily one would administer cases in a random order to average out theeffects of the order in which task materials are presented. However, the casesused in this study were constructed from data published by the SEC in itsaccounting and auditing enforcement releases. These data are publicly available,raising the possibility that auditors may recognize the company and the specificallegations of impropriety by the SEC. In order to disguise the identity of thecompany, new company names were created. In addition, auditors were given acompany in an unfamiliar industry first, followed by a company in their ownindustry. It is still possible that auditors may have recognized the company onsubsequent cases. Our data, however, show no improvement in the rate of frauddetection after the first case. Protocol comments during the study and a post-experimental questionnaire also indicate that none of the auditors recognized thecompany and none of them were aware that the company had been sanctionedby the SEC.

Auditors worked on the cases in their offices and could use any resourcesthey would normally use to perform a concurring partner review. All sessionswere tape recorded. Each auditor signed a consent form to participate and wasgiven a written set of instructions. In these instructions auditors were asked tocomplete three tasks: (1) conduct a concurring partner review and reach a con-clusion with regard to the appropriateness of the engagement partner's judgment


that an unqualified audit opinion should be issued; (2) provide a rating judgmentof the likelihood that the case material justifies an unqualified opinion, severaltypes of qualified opinions, or an adverse opinion; and (3) provide think-aloudcomments while performing tasks (1) and (2).

After reviewing each case, auditors recorded their conclusions by rating thelikelihood of issuing each of six possible audit reports (which comprised thecompeting set of solutions for each case) on a seven-point scale where a ratingof ! meant "very unlikely" and 7 meant "very likely." The six types of reportswere an unqualified opinion, a qualified opinion for noncompliance with GAAP,a qualified opinion for lack of consistency, a qualified opinion for going con-cern, a qualified opiniori for material uncertainties, and an adverse opinion(which is the correct solution in all four cases). Auditors were also asked tochoose the outcome they would recommend for each case.'

A questionnaire was administered to auditors after they had completed allfour cases in order to check background data such as indust|;y specialization,personal experience with fraud cases, and number of years of audit experience.These data served as a check for subject classification to each of the four auditorgroups (experienced partners in medical products, in paper products, and in gen-eral manufacturing, and new partners in medical products).

ResultsEach auditor's thinking-aloud (process) data* were analyzed in two stages. First,the process data v rere examined (or inconsistencies detected, hypotheses used tointerpret these inconsistencies, and detection of management's growth-companyfra-ne. Second, process data were analyzed for the aggregation process used toccmbiiie hypotheses, to make materiaiity judgments, and to detect fraud.Auditors comments and conclusions were scored for explicit comments that thefinenciai statements were rnateriaily misstated due to fraud and/or error.

Based on the data analysis, auditors were classified into three groups.Au;.iitors who proposed a single (functional, error, or irregularity) hypothesis tointerpret each inconsistency were classified as using a single representation.Au.iitors who proposed two or more hypotheses to interpret each inconsistencywere Classified as using multiple representations. Auditors who proposed onestandard hypothesis to interpret all inconsistencies detected were classified asusing ?. standard representation.

A\\ 24 auditors were classified using this procedure on all four cases. The."e-suits of this analysis are presented in Tables 1, 2, and 3 according to what

of representation was used (i.e., single, multiple, or standard). In what fol-ive discuss each group of auditors and present a more detailed analysis

iroiri one auditor in each group.

Auditors who used a single representationThirteen auditors were classified as using a single representation on all fourcases. That is, they proposed a separate (functional, error, or irregularity)hypoiSicsis to interpret each detected inconsistency. These auditors were


deceived by management's frame on all four cases (medical products, paperproducts, strong cues, weak cues). The audit opinions chosen, hypotheses usedto interpret inconsistencies, and number of items aggregated to evaluate incon-sistencies are shown in Table 1.

The data in Table I indicate that all auditors who used a single representa-tion either chose a qualified audit opinion on all four cases (PE 3, GE 5, MN 2),an unqualified opinion on all four cases (PE 4, PE 5, PE 6), or an unqualifiedopinion on some cases and a qualified opinion on other cases (MN 1, MN 4, GE2, GE 3, GE 4, PE 1, PE 2). Choice of these outcomes indicates that auditorswho used a single representation did not detect fraud.

Individual subject analysis (MN 4 and PE 4)To illustrate how these auditors reached audit opinions, a detailed discussion ofthe behavior of MN 4 and PE 4 on the strong-cue medical products case is pre-sented next. These two auditors were chosen for discussion because their proto-col statements illustrate how they were framed by management.

MN 4 began by reviewing the nature of the company's business (the frame).Early in the task he became concerned about a blind alley (litigation) that thecompany was involved in:

I think the one issue being this product liability suit and the company believing thatthey aren't going to suffer any material adverse effect on the financial statements,seems we would want to know how we've tested and arrived at that conclusion andwhat support they have for that.Next, MN 4 detected an inconsistency for research and development. He did

not generate any hypothesis to interpret this cue but commented that he wouldlike to know more about what had happened here:

Kind of curious why there was a drop in research and development from 3 million toL3 million, when it doesn't seem that their product offerings are in the front partdisclosed they had 100 new products or something in there so they're fairly active inthe research and development area and it doesn't seem consistent to have a reductionin research and development costs, so I'd want to know why that change.In his summary, MN 4 was only concerned that the litigation might expand

and become a class action lawsuit that would have severe future consequencesfor the company:

This business of the lawsuit seems to be the type of matter that if you know a prod-uct liability suit may not be insured, if there's some kind of a problem that they'vehad with a lot of their products, this could be a significant issue, so you have to becareful as to how you go forward with that and understand the nature of the productfailure that gave rise to the lawsuit was not something that's occurring everyday outin the field or else you may have some kind of asbestos or dalkon shield on yourhands.

At the end of the task, MN 4 chose a qualified opinion, due to a materialuncertainty (litigation), as his final outcome.

Protocol excerpts indicate that MN 4 was distracted away from the fraud bylitigation early in the task and concluded that litigation was the most significantissue in the case. A review of Table 1 shows that on all four cases MN 4, as well


g

•soa

3

oZ

z

g

3so

6»

I

tm

I

60

I

05

0)

a.

U

sa,

S

zzz3

pa

si

sg

bO

I^^

zz

sia, §

a a, a

s acu a

s a0.. a

s o&. o

% oa. a

S O-a- a

U

Ioo

CL.

o

o

o

o

o

B.

! I i

m

i PU

o

o

o

o

o

o

o

ta

u

tt.

t . O (1.

ttj

ta

m

M

m —

a. oi o

tt- ro

U. ro

Ct.

"• z

tt. rf

tt., —

0.01

B, O)

« -

U £

'¥ s

I - 11

"-

i i

1< - 1

2 .5 -o S -s

k. X C4 d>"

u o


S ca3 • aa uI 3

^5

o

z0- D

0- a

a. a

CL a

fl, a

oicA

u

i83

o

fl.

CL

za

aaz

z

PQ

PU

CL,

W

PQ

U.

CU

a.

cu

1i

Cu

CU

PJ

1

fl. a

z

« z

II,

PQ

1CQ

11

1Cu

1w

w

11

cu

CL,

Cu

UJ !

CU U L

« I

"^ I

[X. tQ

0- pa

£:> 2 g^

o

o

o

o

oZ

o

o

o

o

o

Io

ozo

o

o

o

o

ozoZ

cu uJ ~ z

cu cu tu 7

1 1 l-s l l

UJ

1UJ

1

UJ

cu

uUL

Cu

u.

!

PQ

U- PL

w

UJ

o

o

oZ

o

o

Cu

U-

u.

uUJ

ai

UJ

a, —

o

o

cu

PJ

UJ

cu

CQ

PJ

m

UL

Cu

PQ

PU

UJ

UL

'—'

tN

r-i

c.

O

UJ

c

•o BIS

d B ^ O Mol

dsan

d

^

.1-8fe HXI 00I ^Z

ooZ

o

oZooZooZ

oZo

oZo

ooZ

oZoz

ooZ

oZ

oo

ooZ

oZo

ozo

"B -a


as the other 12 auditors who used a single representation, failed to generateinconsistencies on some of the cues manipulated by management, used errorhypotheses to interpret some inconsistencies, and failed to aggregate inconsis-tencies. The growth-company frame appears to be responsible for the poor per-formance of these auditors.

Some auditors who used a single representation did not appear to be skepti-cal and explained away the inconsistencies they encountered. One of the moreextreme examples of this was auditor PE 4. On the strong-cue medical case,PE 4 detected an inconsistency for each of the critical cues shown in Table I.Despite the use of an irregularity hypothesis to interpret some of these inconsis-tencies, PE 4 chose an unqualified opinion. At the end of the case, PE 4 reliedexcessively on his engagement partner and thus explained away the detecteditems especially the molds and dies cue:

SigEificant factors that affected my ratings. I think the reliaice on the engagementpartner although I would question him. I'm really not familiar with what you dowith molds and dies. ! assume that the change was justified as GAAP but I don'treally know that for a fact so that would mean that the qualified report for non-compliance with GAAP would be very unlikely.The degree to which a concurring partner should rely on the engagement

partner is currently being debated in the professional literature. The NationalCommission on Fraudulent Financial Reporting (Treadway 1987) recommendedthat the concurring partner should be involved in the audit at an earlier stage (inaudit planning) rather than just acting as an independent partner at the end of theaudit. However, Mautz and Matusiak (1988) argued against the Treadway com-mission's recommendation because they felt it would impair the independenceof the concurring partner. Mautz and Matusiak (1988) were concerned aboutbehavior such as that exhibited by PE 4 in the current study. The SEC practicesection (SECPS) of the AICPA has not formally implemented the Treadwaycommission recommendation and is still studying the proposal.

Auditors who used multiple representationsFour auditors were classified as using multiple representations on all four cases.These auditors proposed two or more hypotheses to interpret inconsistencies. Onall four cases these auditors detected management's frame (the growth compa-ny) but failed to detect the financial statement fraud. Table 2 shows the results ofaudit opinions chosen, hypotheses used to interpret inconsistencies, and numberof items aggregated to evaluate inconsistencies.

The data iti Table 2 indicate that auditors who used multiple representationseither chose a qualified audit opinion on al! four cases (ME 1, ME 3, MN 3) oran unqualified opinion on all four cases (GE 1). Choice of these outcomes, aswell as the specific comments made by these auditors, indicates that auditorswho used multiple representations did not detect the fraud.

Individual subject analysis (ME I)To illustrate how these auditors detected management's frame, but not the fraud,a detailed discussion of the behavior of ME 1 on the strong-cue medical


products case is presented next. This auditor was chosen for discussion becausehis protocol comments contain clear statements indicating that he recognizedthat management's frame was misleading. He did not, however, combine thedetected inconsistencies.

In his review of the financial statements, ME 1 detected the molds and diescue manipulated by management. However, after reading the note description ofthis cue he accepted management's explanation of why molds and dies wereaccounted for in this manner and thus was satisfied that the accounting valuationwas appropriate. He used an error hypothesis to evaluate the disclosure of thisitem, evaluated this cue individually for materiality, and proposed that additionaldisclosure was required in the auditor's report due to a change in accountingpolicy:

They've got a change in their handling of costs incurred internally to bring certainproduction equipment to its intended productive capacity so they capitalized 5.7 mil-lion dollars worth of costs which had been previously expensed ... and that's materi-al to the income statement so that would be an accounting change which we woulddeal with in our opinion, this is sort of fact... they've done a study and done this todetermine that it's better ... they probably ought to beef that disclosure up a little bit.

Next, ME 1 detected an inconsistency for research and development. Hetried to generate multiple (functional and error) hypotheses for this inconsisten-cy but could not come to any conclusion on which was the more likely interpre-tation. The cue was detected but remained unresolved:

Research and development dropped from 3 million to 1.3 million which is hard toimagine given the fact that research and development of new products are their lifeblood so, I don't quite understand that that could mean they've made a big attemptto hold the line on eamings, cut the R and D, or I suppose it could mean they justmisclassified something, or are accounting for it wrong, or trying to capitalize it, or Idon't know.

Next, ME 1 detected a change in an accounting estimate. He again used anerror hypothesis to evaluate this cue and considered it to involve the same (con-sistency) issue as the molds and dies cue encountered earlier. He thus combinedthese two cues and evaluated them for materiality by comparing them to pre-taxoperating income. At this point he detected management's frame and realizedthat if he undid some of management's discretionary accounting changes, theoperating income of the company changes considerably:

We had a change in accounting estimate that had the effect of increasing incomebefore tax by 1.2 million roughly so if I adjust the income statement for that factorplus the accounting change you end up with a drop of 50 percent of income beforetax, a little different picture than you got before.In his summary, ME 1 indicated that he saw signs of financial difficulty

despite management's attempts to mask them but did not consider managementto have done anything wrong or improper because GAAP allows managementdiscretion in making accounting estimates and accounting policy choices:

So in a sort of summary fashion 1 guess it looks like the sort of high tech company,fairly hefty growth rate, they're not putting much of it to the bottom line, they' ve gota balance sheet problem in terms of managing their working capital needs ... 1 don'tsee anything here that would pUsh a panic button but there's some early warning


3e

<uS,

I

siQ.

U

u

a.

S i

a. O

a, a

a. a

s ca. a

a. a

s oa. a

i

F/E

^F/

EF/

EF/

EF/

E

gB-

F F/

EF

F/E

tu

tu

tL,

tL.

g

F/E

F/E

F/E

F/E

F F/

EF/

E

F

tu

tu

F/E

1

i1

I

1

F/E

F/E

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

F/E

F/E

F/E

H/d

F/E

F/E

F/E

F/E

F/E

F/E

F/E

F/

EF/

E E

tu

p-

tu

F/E

tu

tu

B U

1

i

g

" g

•6 8

u o

t' 3c

si u3 S:U — CO

U

.5 I60

11O Q

gg

tu

_ o

„ o

__ o

o

o

o

o

o- z_ o

o

z

o

o

z

o

•.~ m o

• a

•a '^ a. =« « J=

iS a: * Q


signs of business issues as opposed to accounting issues... I mean if you look at justkey numbers it looks like a great balance sheet and then you start digging into it andit isn't so.

Protocol excerpts indicate that ME 1 used multiple hypotheses to interpretcues and attempted to evaluate the significance (materiality) of each individualcue. This approach was partly successful. ME 1 detected management's frameof a successful growth company and recognized that the initial impression of thecompany was misleading. He accepted management's explanations, however,for individual inconsistencies as the normal exercise of judgment as allowed byGAAP. On a rich complex task, there are many possible competing interpreta-tions, and detecting the overall frame is not sufficient for detecting the fraud. Anexplicit attribution of management's intention to deceive users of financial state-ments appears to be required to detect fraud.

An analysis of the cue interpretations indicates that these auditors detectedseveral inconsistencies in all four cases. Management's frame appears not tohave been effective in suppressing the detection of inconsistencies for theseauditors. In particular, these auditors detected a deterioration in the operatingincome of the company and thus recognized that the company was in decline butappeared to be a growth company only because of a series of discretionaryaccounting policy choices. These auditors thus detected management's overallgrowth-company frame.

Auditors who used multiple representations, for the most part, did not fol-low auditing standards (HB 5130, SAS 47), which require them to combine adiverse set of inconsistencies. In addition, some of these auditors routinelyconsidered the possibility that the inconsistent item was in compliance withGAAP (not fraudulent). For example, on the strong-cue medical case ME 1 pro-posed an irregularity hypothesis for several cues but failed to aggregate them. Atthe end of the case, he focused on two cues (molds and dies and the change inaccounting estimate) but was unwilling to choose a noncompliance with GAAPopinion, because he thought it was possible that the accounting treatment wasappropriate:

I think the disclosure about the change in capitalizing internal costs and the changein estimate are very important. Without those changes they would have had a reduc-tion in income between years ... non-compliance with GAAP, I think its prettyunlikely assuming these things tum out to be appropriate.These results suggest that it is possible for auditors using multiple represen-

tations to detect a growth-company frame but that such an approach is not suffi-cient for detecting fraud or noncompliance with GAAP. After the overall com-pany frame has been detected, an additional inference has to be made aboutmanagement's intention to violate GAAP. The use of multiple representationsfor each cue makes it very difficult to infer management's intent unambiguouslyor to conclude that the financial statements are not in compliance with GAAP.

In the present study, detection of management's successful growth-compa-ny frame was not a sufficient condition for the auditor to question the integrityof management. Auditors who used multiple representations generated mostly


functional or error hypotheses, rather than irregularity hypotheses, to interpretinconsistencies detected. This lack of skepticism (CICA 1991a, AICPA 1988a),a concern that there may be a legitimate explanation for the inconsistencydetected, and the lack of an aggregation procedure (CICA 1988, AICPA 1984),may have led to a failure to detect fraud.

This pattern of generating mostly functional explanations has been docu-mented in several research studies (e.g., Koonce 1992; McMillan and White1993; Kaplan, Moeckel, and Williams 1992), and the failure to aggregate errorshas been cited as a major deficiency found in peer reviews conducted by thePublic Oversight Board (Evers and Pearson !989). The behavior of auditors inthis study is thus consistent with behavior observed in experiments as well asfield investigations.

Auditors who used a standard representationSeven auditors used a standard representation to interpret the data in each of thefour cases. Six of these auditors had specialized in the medical products indus-try, and one auditor had specialized in audits of general manufacturing compa-nies. Results of audit opinions chosen, hypotheses used to Interpret inconsisten-cies, and number of items combined to evaluate inconsistencies for these sevenauditors are shown in Table 3.

The data in Table 3 indicate that in all but 2 of the 28 instances (7 subjects x4 cases), auditors who used a standard representation chose either an adverse ora Fsoncompliance with GAAP opinion as their outcome. Choice of these twoopinions indicates that the financial statements are not presented fairly inaccordance with GAAP (they are misleading, and GAAP have been violated).

Individual subject analysis (MN 5)To illustrate how these auditors reached audit opinions, a detailed discussion ofthe behavior of MN 5 on the strong-cue paper case is presented next. This audi-tor was chosen for discussion because his protocol comments contain clear state-ments that indicate the use of a single (irregularity) hypothesis, a common unitof atiaiysis (dollar impact of inconsistencies on pre-tax income), and an aggrega-tion procedure that evaluated inconsistencies cumulatively for materiality.

MN 5 began by developing an understanding about the nature of the compa-ny's business by reading the description of the company (frame) constructed bymanagement in the 10 K report. In his summary, prior to reviev/ing the financialstatements, MN 5 indicated that he felt very comfortable with the company:

It's a company that has been in business a long time. From a product standpointthere's nothing I'm concemed about that much here ... I feel relatively comfortableright now and I'm ready to dig into the financial statements.Next, MN 5 began to review the financial statements and compared the data

to his expectations. MN 5 detected an inconsistency in the capitalization of inter-est expense. This cue violated his expectation of management's intention (goal)to jninimize taxes and triggered an irregularity hypothesis. The interest capitalizai-lion cue provided disconfirming evidence for his expectations for the company:


^ 5

GE

6

NO

Z

MN

5

so01

<n

CU

o

Subj

ect:

ik

Stro

ng

bO

a

/eak

S

tn/e

sJc

Stro

ng

V

^

/eak

S

tnSt

rong

V

Wea

k/e

ak

Stro

ng

bO

a

Wea

kC

ue t

ype:

g

z

a

s g«- gS gCL ^

z

CL

CL

CL

go

gggo

<<

cu

w

— w

u

E/l U]

cu B-

£• SP P

ii P3

0.

pa

cu cu ~

cu

a. <

S <

CL <

S <

0. <

oo

0 o o a X ob.ia.

uCA

uCA

> -

CA(U

>-

U

CA

uCAID

11

u

E/I

cu

w

CL

U —'

«1

( 0 — 1 —

•— —• >n

IQ

I2 c « oo « K -a

V3

O

Z

Yes

Yes

>-CA

u

>-

CA

u>-

>CA

> -

CA

w

cu

mffl

"^

—

-

CU

«

PL,

U

Cl..

•-^

—

0 0

i n

OO

r~

NO

ON

>n

NO

m

u

Yes

>-CA

> ^

u> •

CO

> •

CO

> ^

CO

CA

CO

> •

1cu

1

—

—

ON

m

NO ^

m

0 0

so

OO

NO

> -

Yes

Yes

o

CO

> ^

>•

c«

> ^

Yes

O

Detecting Framing Effects in Financial Statements 10!

The company seems to be stretching for earnings here. The reason why I say that isthat they had a tremendous increase in the amount of capitalized interest... A lot ofcompanies wili set very high project levels before they'll qualify something forFASB 34 [Financial Accounting Standards Board] ... they just don't want to do it. Ifyou capitalize it for books you're going to have to do it for tax purposes, so for mostcompanies cash flow is more important to them than trying to boost eamings.

The cue value for molds and dies also violated MN 5's expectation for apaper company. Again he used the same (irregularity) hypothesis (see Table 3)to i.ntcrpret this cue:

Vn\ reading that approximately $2,890,000 of engineering labor and other costswere reco.rded as part of the machinery and equipment account. The question Iwould ask the [engagement) partner is have we tested that $2,890,000 for overstate-ment. You got to be worried that this is like capitalizing research and developmentexpense ... St's certainsy a way to improve operating results so that is the secondmajor instance now

At this poinS in the prob!em-s,o!ving process, MN 5 was concerned becausehe inad detected two inconsistencies that indicated the company's stated financialperformance was created by manipulating the use of accounting procedures(GAAP)., rather than by operating activities of the company (detected thegn wth-company frame). Next, MN 5 detected an inconsistency in research anddevelopment. Once again he used the same (irregularity) hypothesis (see Table3) io interpret this cue and became distressed because this cue indicated manage-ment was deliberately violating GAAP to overstate the reported income of the

'> see R & D which we have to expense after FASB 4, dropped from $3.9 million to$1.7 miiiion so F ve got a drop here of about the same dollar amount that in the priorpage [moids and dies] I capitalized. That really bothers me ... probably most of thepeople in the engineering department found a new place to charge their costs thepast year. That is really bothersome.

in his summary, MN 5 listed six items: foreign currency, molds and dies,interest capitalization, research and development, operating income, and achange in esiirriate (sec Table 3) as issues that needed to be followed up with theengagement partner. At the end of the case, all the inconsistencies identifiedwsre converted into a dollar value impact on pre-tax income, which was theconmon unit of analysis, and were then compared collectively to operatingearnings to detemiine materjaHty. Due to the very material impact of the incon-sisc.:;ncies detected and his inference that management was deliberately violatingGA AP, he chose an adverse opinion as his outcome:

Very unlikely that there'd be an unqualified report and the reason for that is the.s.heer magnitude of the accounting problems here ... the dollar magnitude is toolarge. I th.ink its uniike'ry we could do that. What I'm going towards is an adverseand the reason is that $8.7 million of problems in relation to $16.7 million of incomein a publicly held company, anytime you lose half your income and then you stilldor.'T feel good because there are other things in here that appear to be a stretch ando dcilar magnitude or errors is a problem.

These protocol excerpts indicate that MN 5 was comfortable with the com-pany after reading the narrative description of the company's business (frame)


constructed by management. Subsequently, MN 5 detected several cues thatwere inconsistent with his expectations. The inconsistency in the interest capital-ization cue made him suspicious of management's growth-company frame andled him to use an irregularity hypothesis to interpret this inconsistency. He thenused the same (irregularity) hypothesis to interpret subsequent inconsistenciesdetected, converted them into a standard unit of analysis (dollar impact of incon-sistencies on pre-tax income), aggregated all the inconsistencies, and evaluatedthem cumulatively for materiality.

On both the paper and medical products cases, MN 5, as well as the othersix auditors who used a standard representation (see Table 3), detected severalinconsistencies, aggregated all the inconsistencies identified, converted theminto dollars, and concluded that the cumulative dollar value of the inconsisten-cies had a material impact on the financial statements. On the basis of this cumu-lative pattern of inconsistencies detected, these auditors detected the financialstatement fraud. These auditors were able to convert the data of both the paperproducts and medical products cases into a standard unit of analysis: dollarimpact of inconsistencies on pre-tax income. This result supports Kahneman andTversky's (1986) proposal that persons who develop a standard representationare able to detect framing effects successfully. These results also indicate thatthe fraud can be detected by auditors who use a standard risk hypothesis (erroror irregularity) and the aggregation procedure specified in auditing standards(CICA 1988, AICPA 1984).

CondusionThe results of this study are based on a small sample of experienced auditorswho were simulating a concurring partner review. The results support Kahne-man and Tversky's (1986) proposal that framing effects can be detected bytransforming alternative versions of a problem into a standard representation.All (seven) auditors who used a standard representation in this study successful-ly detected both management's frame and the financial statement fraud in allfour cases.

Our results provide weaker support for a second approach proposed byKahneman and Tversky (1986) for detecting framing effects, namely, the use ofmultiple representations of a problem. Four auditors used this approach in thecurrent study. These auditors detected management's overall company frame,but not the financial statement fraud in all four cases. In a complex task environ-ment like auditing, there are several possible interpretations for a given cue, andthese auditors did not integrate a seemingly diverse set of cue interpretations.These results suggest that a strategy that uses multiple representations may notbe effective in complex task domains such as auditing.

Auditors who used a single representation were unable to detect either theframe or the fraud. The results presented here provide strong support for thepower of framing effects. Despite their motivation, training, and experience,over half (13) of the audit partners who participated in this study were deceivedby management's frame.


The Treadway Commission (1987) examined the policies of public accounting firmsin assigning concurring partners to audits and found a considerable amount ofvariability among audit firms. Some firms require SEC and/or industry experience,whereas other firms do not require such experience. The Treadway Commission alsofound no explicit seniority (number of years experience) requirement for assignmentof an auditor to the concurring review partner function. Some firms explicitly labelcertain partners as "experts" in conducting concurring partner review, whereas otherfirms allow al! partners to participate in the concurring partner review function.Due to the small sample of partners participating in this study and considerablevariability in the policies of their firms in designating concurring review partners.we do not designate any individuals as "experts" in this task. We only report theindustry specialization and number of years of audit experience as backgroundvariables and not as proxies for expertise.

Auditors who had experience in the medical products industry solved the weak-cuepaper products case first (P-), followed by the weak-cue medical products case(M-), the strong-cue paper products case (P+), and the strong-cue medical productscase (M+). Auditors who had experience in the paper products industry solved theweak-cue medical products case first (M-), followed by the P-, M+, and P+ cases.Auditors who had experience in genera! manufacturing solved the cases in the sameorder as auditors who had experience in the medical products industry (P-, M-, P+,M+).

Auditors were asked to rate the various audit opinions possible for each case toassist them in summarizing the key issues in the case and to reach a conclusion as towhat audit opinion to recommend. The number of items mentioned in thesesummaries and the specific audit opinions chosen constitute the outcome data of thestudy. Subsequent to the administration of these cases, audit reporting standardswere changed so that various qualified opinions in the current study (e.g., qualifiedopinion for going concern) would now be reported as an unqualified opinion with anadditional paragraph to disclose going concern, lack of consistency, or othermaterial uncertainties.

Each protocol (N = 96) was analyzed for cues, the interpretation of cues, aedconclusion(s) by Karim Jama!. In order to establish the reliability of the scoringprocedure, a second coding was performed by Glen Berryman. Due to the largevolume of protocols (N = 96, and the average protocol is 30 pages) a stratifiedrandom sampling plan was created. Auditors were classified into four groups basedon their industry specialization (experience in the medical industry, in the paperproducts industry, in general manufacturing, and new partners in the medicalindustry). Each group had six auditors. The second coder analyzed at !east oneprotocol from each of the four groups of auditors on each case. In addition, thesecond coder analyzed one protocol for every auditor (for a total of 24 protocols).For each of the 24 protocols analyzed, the second coder did a complete analysisconsisting of an identification of cues, an interpretation of cues, and the conclusionreached by the subject. Each protocol was coded in two stages. In the first stage,each coder independently identified a list of cues in a protocol. Then the two codersreconciled their list of cues and created a common list of cues. Then the two codersindependently identified the cue interpretations and conclusions reached by subjects.

The average proportion of agreement between the two coders was 87 percent foridentification of cues, 78 percent for interpretation of cues, and 78 percent for con-ciusions reached by subjects. A statistical coefficient of agreement developed byCohen (1960), called Cohen's K, measures the proportion of agreement between twosCorers in placing items into a set of/T unordered categories. Cohen's K wascalculated fer the interpretation of cues and conclusions reached by subjects. The


value of A! was 0.76 for interpretation of cues, and 0.75 for conclusions reached bysubjects. According to Cohen, K values can be transformed into Z scores. When thisis done, both K values are significant at/x.OOOl. Differences in scoring wereresolved by discussion between the two coders. In all cases, the two coders wereable to reach an agreement regarding the scoring differences under review.

ReferencesAmedcan Institute of Certified Public Accountants. Statement of Auditing Standards No.

47: Audit Risk and Materiality in Conducting an Audit. AICPA, 1984.. Statement of Auditing Standards No. 53: The Auditor's Responsibility to Detectand Report Errors and Irregularities. AICPA, 1988a.. Statement of Auditing Standards No. 58: Reports on Audited Financial Statements.AICPA, 1988b.

Bedard, J.C., and S.F. Biggs. Pattern Recognition, Hypotheses Generation, and AuditorPerformance in an Analytical Task. The Accounting Review (July 1991), 622-642.

Canadian Institute of Chartered Accountants. CICA Handbook Section 5130: Materialityand Risk in Conducting an Audit. CICA, 1988.. CICA Handbook Section 5135: Auditor's Responsibility to Detect andCommunicate Misstatements. CICA, 1991 a.. CICA Handbook Section 5510: Reservations in the Auditor's Report. CICA,1991b.

Chi, M., P. Feltovich, and R. Glaser. Categorization and Representation of PhysicsProblems by Experts and Novices. Cognitive Science (Vol. 5, 1981), 121-152.

Cohen, J. A Coefficient of Agreement for Nominal Scales. Educational andPsychological Measurement i\ol 20, 1960), 37-46.

Evers, C.J., and D.B. Pearson. Lessons Learned from Peer Review. Journal ofAccowntoncy (Apdl 1989), 96-105.

Gibbins, M., and K. Jamal. Problem-Centered Research and Knowledge-Based Theory inthe Professional Accounting Setting. Accoimting, Organizations and Society(Vol. 18, No. 5 1993), 451-466.

Healey, P.M., and K.G. Palepu. The Effect of Firms' Financial Disclosure Strategies onStock Pdces. Accounting Horizons (March 1993), I-l 1.

Johnson, P.E., K. Jamal, and R.G. Benyman. Effects of Framing on Auditor Decisions.Organizational Behavior and Human Decision Processes (Vol. 50, 1991), 75-105.

Kahneman, D., and A. Tversky. Prospect Theory: An Analysis of Decision under Risk.Econometrica (Vol. 47, No. 2 1979), 363-391.. Choices, Values and Frames. In Judgment and Decision Making AnInterdisciplinary Reader, ed. H.R. Arkes and K.R. Hammond. CambridgeUniversity Press, 1986.

Kaplan, S.E., C. Moeckel, and J.D. Williams. Auditors' Hypothesis PlausibilityAssessments in an Analytical Review Setting. Auditing: A Journal of Practice andr/ifiory (Fall 1992), 50-65.

Koonce, L. Explanation and Counter Explanation Dudng Audit Analytical Review. TheAccounting Review (January 1992), 59-76.

Mautz, R.K., and L.W. Matusiak. Concurdng Partner Review Revisited. Journal ofAccountancy (March 1988), 56-63.

McMillan, J.J., and R.A. White. Auditors' Belief Revisions and Evidence Search : TheEffect of Hypothesis Frame, Confirmation Bias and Professional Skepticism. TheAccounting Review (July 1993), 443-465.


McNeil, B.J., S. Pauker, H. Sox, and A. Tversky. On the Elicitation of Preferences forAlternative Therapies. New England Journal of Medicine (Vol. 306, 1982),1259-1262.

Newell, A., and H.A. Simon. Human Problem Solving. Prentice Hail, 1973.Thaler, R. Toward a Positive Theory of Consumer Choice. Journal of Economic

Behavior and Organization (Vol. I, 1980), 39-60.Treadway, J.C. Report of the National Commission on Fraudulent Financial Reporting.

AICPA, 1987.

Documents

Detecting Framing Effects in Financial Statements