The current issue and full text archive of this journal is available at www.emeraldinsight.com/1475-7702.htmRAF10,1 Auditors’ going-concern judgments: rigid, adaptive, or both?30 Andrew J. Rosman University of Connecticut, Storrs, Connecticut, USA Abstract Purpose – The purpose of this paper is to examine when auditors’ decision behavior is rigid and adaptive in the going-concern judgment. Because rigid behavior has been found to produce inappropriate outcomes, understanding when decision behavior is rigid or adaptive can lead to improved decision making. Design/methodology/approach – An experiment is conducted using cases based on real companies to produce information search traces as dependent measures that are studied in the ill-structured and structured parts of the going-concern task. Findings – Auditors are adaptive in ill-structured tasks and rigid in structured tasks as predicted by theory. Evidence of ﬂawed decision making commonly found in studies of ﬁxation and related concepts was not found. Research limitations/implications – The ﬁndings suggest the importance of explicitly accounting for task structure when studying decision behavior in situated contexts. Future research could assess whether task structure similarly impacts behavior in non-auditing contexts. Practical implications – Researchers and practitioners have long been concerned about inappropriate rigid behavior. This paper helps practitioners better understand when rigid or adaptive behavior is likely to occur to improve decision making. Originality/value – Taking a novel approach to reconcile two well established but conﬂicting bodies of literature by focusing on “when” not “whether” people are rigid or adaptive, this paper resolves a long-standing paradox. The implication for the literature is that reframing the question and directly measuring behavior demonstrates that individuals are neither rigid nor adaptive, but can be both as they follow behavior that is consistent with the demands of the task when the demands are deﬁned in terms of task structure. Keywords Auditors, Decision making, Cognition, Individual behaviour, Going concern value Paper type Research paper I. Introduction Research on ﬁxation and related concepts in the cognitive and Gestalt psychology literatures focuses on whether decision makers learn rigid decision behaviors (i.e. persistently use a previously developed approach to solving a problem) or adaptive decision behaviors (i.e. modify their decision processes to changes in information), and generally ﬁnd the former in accounting and other contexts (Ashton, 1976; Barnes and Webb, 1986; Bloom et al., 1984; Chang and Birnberg, 1977; Dearborn and Simon, 1958). But, might experienced decision makers also learn adaptive behaviors, and might some rigid behavior be appropriate? While the empirical evidenceReview of Accounting and Finance suggests otherwise, some question the research focus used to derive that evidence.Vol. 10 No. 1, 2011pp. 30-45 Gibbins and Jamal (1993, p. 453) observed that research “has led to theory developmentq Emerald Group Publishing Limited [. . .] of the persons who carry out a speciﬁc kind of task rather than a theory of the task1475-7702DOI 10.1108/14757701111113802 itself [. . .].” They, therefore, propose “shifting the focus more toward the task setting
to which they may adapt.” Similarly, others warn that research that fails to consider the Auditors’environment or setting will miss important determinants of performance since going-concernenvironmental factors affect motivation, knowledge, and ability (Libby and Luft, 1993). One way to shift the focus from the individual to the task setting in which decision judgmentsmakers learn is to reframe the research question to ask what is it about novel (routine)situations that would cause decision makers to learn to be adaptive (rigid) and when isbeing adaptive (rigid) appropriate? In other words, rather than ask whether individuals’ 31behavior is rigid or adaptive the question becomes under what conditions or when willindividuals learn to be rigid or adaptive? To investigate how task setting may affect howrigid and adaptive behaviors are learned, this paper uses theories of situated cognitionand adaptive behavior to examine the role of task structure in determining rigid andadaptive decision making by auditors in structured and ill-structured tasks. Theories of situated cognition and adaptive learning are used to examine whenauditors learn rigid and adaptive behavior relative to changes in task structure that areconsistent with recommendations by situated cognition theorists (Elsbach et al., 2005) aswell as learning theorists who caution that: [. . .] a theory of thinking and problem solving cannot predict behavior unless it encompasses both an analysis of the structure of task environments and an analysis of the limits of rational adaptation to the environment (Newell and Simon, 1972, p. 55).Auditor judgment is well suited in this study because it is learned through formaltraining and by accumulating on-the-job “situated” experiences working in small groups(i.e. audit teams). The distinctive two-stage going-concern judgment is an ideal task forstudying adaptive and rigid behavior because of the way the stages are structured. Theﬁrst stage (evaluation) is “ill-structured”: auditors are not restricted in the type ofinformation acquisition they engage into evaluate whether they have substantial doubt,but rather can freely apply intuition based on experience to adapt informationacquisition. Should they express substantial doubt, they proceed with the second stage(audit opinion), which is “well structured.” Here, auditing standards require thatauditors rigidly follow speciﬁc rules that restrict their decision making, even limiting thetype of information that they can consider in rendering their ﬁnal judgment. I ﬁnd that auditors were adaptive in the ill-structured component of the going-concerntask and rigid in the well-structured component. The implication for the literature is thatreframing the question and directly measuring behavior demonstrates that individualsare neither rigid nor adaptive, but can be both as they follow behavior that is consistentwith the demands of the task when the demands are deﬁned in terms of task structure. The next section summarizes the two competing literatures on rigidity and adaptivebehavior and discusses how each relates to task structure. Section III describes situatedcognition as a framework within which rigid and adaptive behavior can be betterunderstood. Section IV provides a task analysis of the two-stage going-concernjudgment and develops hypotheses. Section V describes the experiment. The results ofthe experiment and related statistical analysis are presented in Section VI. Finally,Section VII offers a discussion of results and concluding observations.II. Rigidity, adaptability, and task structureResearch on rigidity and adaptation dates back over 70 years to Gestalt psychologistswho investigated “insight problems” (i.e. problems for which participants do not have
RAF a solution available from memory and for which there was no available schemata10,1 that guaranteed a solution). They were the ﬁrst to theorize that as individuals gain experience with a task, their knowledge acts as a mental set that limits their search for possible solutions, and thus promotes rigid decision processes. One of the earliest demonstrations of this effect is Maier’s two-string problem (Maier, 1931), where participants were unable to solve the problem because of their ﬁxation on the usual uses32 for an object. Subsequent “insight” studies replicated and extended this result (Ashton, 1976; Chang and Birnberg, 1977; Duncker, 1945; Maier, 1931; Marchant, 1990). Cognitive psychologists and decision process researchers extended the relevance of the rigidity problem to ill-structured tasks (Anderson, 1990; Cyert and March, 1963; Frensch and Sternberg, 1989; Holyoak, 1991; Katz, 1982; Mason and Mitroff, 1981; Rosman et al., 1994, 1999; Turner, 1976; Walsh, 1988). Task structure is the extent to which the components of a task are well organized, interrelated, and understood (Abdolmohammadi, 1999; Bowrin, 1998; Prawitt, 1995) and can be deﬁned in terms of the number of constraints left unspeciﬁed by the initial problem statement (Reitman, 1965). Structured tasks are well organized and understood so they typically are handled in a routine or rigid way because it is appropriate to persistently apply the same schema. Ill-structured tasks “have not been encountered in quite the same form” so that “no predetermined and explicit set of ordered responses exist in the organization” (Mintzberg et al., 1976, p. 246). That is, by deﬁnition ill-structured tasks are novel and are more likely to be approached by the decision maker in a ﬂexible or adaptive way because the decision maker understands that persistent and stable schemas would produce ﬂawed decisions (Elsbach et al., 2005). Consistent with the Gestalt psychologists’ studies about insight, most studies concluded that experienced decision makers, when confronted with an ill-structured problem, will tend to rigidly rely on their prior domain-speciﬁc knowledge, rather than adjust their behaviors to the speciﬁc information presented in problems. Examples include study of chess masters (Chase and Simon, 1973), bridge champions (Frensch and Sternberg, 1989), physicists (Chi et al., 1981), and tax consultants (Marchant et al., 1991). From these and other studies have come warnings about rigidity and its analogues, selective perception (Dearborn and Simon, 1958), perceptual screens (Cyert and March, 1963), functional ﬁxedness (Katz, 1982), tunnel vision (Mason and Mitroff, 1981), collective blindness (Turner, 1976), escalation of commitment (Staw, 1981), and commitment to the status quo (Geletkanycz and Black, 2001). Perhaps, the most widely cited study among those listed above is one on functionally trained decision makers (Dearborn and Simon, 1958), which found that when experts’ domain-speciﬁc knowledge does not efﬁciently map onto the structure of a decision task, they tend to display a “departmental” bias; that is, functionally trained decision makers (e.g. those employed in the marketing department) will tend to identify organizational problems in terms of their speciﬁc functional experiences, regardless of the nature of the problem. Notwithstanding this extensive literature, another well-established body of research shows that individuals have the capacity to adapt behaviors in the short term to respond to unique contexts, to create new knowledge to solve problems over the intermediate period, and to evolve to a new way of thinking through learning over a longer period of time (Newell and Simon, 1972; Simon, 1981). Moreover, characteristics of tasks, including setting and structure, are critical in understanding human behavior.
Analyzing the demands of task environments enables researchers to examine both Auditors’whether and when individuals adapt. going-concern A well-structured task can engender appropriate rigid decision processes becausedecision makers, who have had repeated experience with that task structure, should judgmentsrecognize that their domain-speciﬁc knowledge efﬁciently (at minimum cost) maps ontothe structure of the decision task (Shanteau, 1992; Stewart et al., 1997). That is, becausewell-structured decision tasks have few unspeciﬁed constraints, the domain-speciﬁc 33knowledge learned by experienced decision makers should prompt them to cut shortinformation search and rigidly (but appropriately) jump to solutions used in the pastwithout much conscious thought. In contrast, ill-structured tasks should engenderappropriate non-rigid decision behaviors because they are not as constraining. Nonetheless, few studies explicitly account for task structure when analyzing decisionbehavior other than performance. This omission may be because the focus of most studiesis on the question of whether experienced decision makers display rigid decision behaviors,rather than on when, or under what task conditions, rigid behaviors are appropriate(Gibbins and Jamal, 1993; Libby and Luft, 1993). Therefore, the many warnings aboutrigidity that frequently appear in the literature may be an artifact of the research design.III. Situated cognition for rigid and adaptive behaviorsSituated cognition, which is “thinking that is embedded in the context in which it occurs”(Elsbach et al., 2005, p. 423), suggests that rigid behavior results when pre-existingschemas are used that do not reﬂect or react to changes in situation or context. Being ableto identify when interactions between schemas and context produce ﬂawed decisions(e.g. inappropriate rigid behavior) is critical to learning how to “manage cognitiveprocesses” (Elsbach et al., 2005, p. 431) in successful organizations. The ability to do sooften is derived from actively engaging a professional work environment where learningis tacit from personal and shared experience among colleagues rather than exclusivelyfrom formal and passive training (i.e. “social interaction” (Contu and Willmott, 2003) and“knowing in action and in practice” (Handley et al., 2007). Situated cognition is related to the concepts of situated learning (Brown et al., 1989;Grifﬁn, 1995) and legitimate peripheral participation (Lave and Wenger, 1991) inthe education literature. Situated cognition promotes active rather than passive learningthat is contextual rather than isolated and abstract (Herbert and Burt, 2004; Handley et al.,2007). For instance, learning vocabulary from use in conversation with others is moreeffective than rote memorization by oneself from a dictionary. Situated cognitionsuggests among other things that schemas tend to be relatively stable and persistent(i.e. rigid) unless particularly novel situations are encountered (Elsbach et al., 2005). In contrast to the research on adaptive behavior, empirical studies of situatedcognition in education focus on novice behavior (Brown et al., 1989; Grifﬁn, 1995) withthe goal of developing experience-based schemas through approaches such asmentorship. Research on more experienced decision makers tends to be limited toexamples of bounded or rigid behavior (Elsbach et al., 2005), perhaps because situatedcognition is rooted in the education literature. Thus, research needs to extend the theoryof situated cognition to more comprehensively address the rigid and adaptive behaviorof experienced decision makers in real-world contexts. Bridging the theories of situated context and adaptive behavior, I draw on the insightsfrom Holyoak (1991), who distinguishes between “routine” and “adaptive” experts. Holyoak
RAF reasons that the expertise of routine experts is based on mastering foundation skills, rules,10,1 and procedures in familiar (i.e. routine) tasks and settings, while adaptive experts rely more on an abstract understanding that can be applied to both familiar and unfamiliar tasks and settings. Adaptive experts develop a deeper conceptual understanding of the task and context because of the richness of their learning environment, which is more “variable” and “unpredictable” than the “stereotyped” environment of the routine expert (i.e. more34 unbounded and ill-structured) (Holyoak, 1991, p. 310). In short, adaptive experts have a larger set of experiences in different situations that enable them to adapt to the current context. Although Newell and Simon (1972) suggest that everyone with high domain knowledge has the capacity to adapt to both structured and ill-structured task demands, it may be that only those who have learned or acquired domain knowledge in ill-structured tasks are able to “invent new procedures” to ﬁt the challenge of ill-structured tasks (Holyoak, 1991, p. 310 emphasis added). Thus, being exposed to ill-structured situated contexts is critical to developing the ability to adapt. Consistent with Holyoak (1991), chess masters (Chase and Simon, 1973), bridge playing experts (Frensch and Sternberg, 1989), and physicists (Chi et al., 1981), who rely primarily on a mastery of routinized knowledge, are routine experts. In contrast, various types of business functional experts, including marketing decision makers (Beyer et al., 1997) are adaptive experts. The latter acquire their expertise from repeated exposure to complex tasks in ill-structured (unbounded) learning environments, each with some degree of underlying structure, but clearly with a moderate to high degree of ill structure. Adaptive experts use a combination of rigid and non-rigid decision processes depending upon the mix of ill-structured and well-structured informational cues contained in the decision task (Holyoak, 1991). Routine experts, on the other hand, inappropriately apply routine (ﬁxated or rigid) decision processes to ill-structured tasks and settings because they are more bound to procedural knowledge, and thus are less aware of unspeciﬁed constraints in that task. Thus, it follows that individuals should follow similar procedures across structured tasks because the task structure requires continued use of the same routine response. In contrast, individuals should adapt procedures across ill-structured tasks, which demand different responses. IV. Task analysis and hypotheses The going-concern task has two stages (evaluation and opinion) that involve the same judgment (i.e. how doubtful the auditor is about the ﬁrm’s continued existence) but contain very different core constraints or task structures. Recall that ill-structured tasks are unique, address problems that have few or speciﬁed guidelines, and need judgment and insight to resolve, while well-structured tasks as are routine, address well-deﬁned problems with well-deﬁned alternatives, and require little judgment. The literature identiﬁes the ﬁrst stage as “ill-structured” and the second as “structured” (Abdolmohammadi, 1999; Prawitt, 1995). In stage 1, the auditor assesses whether there is substantial doubt that the ﬁrm will remain a viable going concern. The ofﬁcial accounting literature neither deﬁnes the going-concern concept nor speciﬁes how to evaluate whether there is substantial doubt, but ambiguously leaves the judgment to be based on the aggregate data collected in the audit.
As one practitioner explains, “[b]ecause the assumption itself is not deﬁned, there are Auditors’wide-ranging interpretations of what an exception comprises” (Venuti, 2004, p. 42). going-concern At the end of stage 1, the auditor arrives at a preliminary judgment of the ﬁrm’sgoing-concern viability by deciding what information is salient, whether the information judgmentssuggests a problem that might threaten the ﬁrm’s status, and, if a problem is detected,what the cause might be. As such, the task requires decision processes that are inherentlyiterative, complex, and unbounded. Because every ﬁrm is unique and may contain many 35problems and causes that are too subtle to detect with simple ratio analysis, the auditormust look into the qualitative context of the business that frames and gives meaning tothe numbers, without the aid of any speciﬁc audit procedures. The evaluation stage isthus ill-structured because it involves an unconstrained task that is not well deﬁned. Should the evaluation in stage 1 suggest substantial doubt about viability, then beforeissuing a ﬁnal report, the auditor must judge how effective management’s plans are foraddressing the ﬁrm’s core problems. This stage, which results in an audit opinion, isbounded by the ﬁndings of the evaluation stage (i.e. the need to consider management’splan arises solely in reaction to a ﬁnding of substantial doubt in the ﬁrst stage). Forinstance, if liquidity is the area of concern, the auditor analyzes and tests viability withrespect to liquidity only. Because rules and procedures limit the actions that can be takenby the auditor, this second (“opinion”) stage is thus constrained and well structured. Putdifferently, stage 2 is well deﬁned because the problems that might affect the type ofopinion to be issued have been deﬁned in stage 1, and in turn, they lead to clearlyarticulated steps to assess the ability of management to mitigate the problems. Building upon the theoretical frameworks of situated cognition and adaptivebehavior and the task analysis of the going-concern judgment, I hypothesize that: H1. Auditors will use adaptive decision processes for ill-structured situations (evaluative stage). H2. Auditors will use rigid decision processes for well-structured situations (opinion stage).V. ExperimentSample and task descriptionThe sample consists of 23 auditors from international accounting ﬁrms who hadattained the status of senior or manager to help ensure that each had expertise with thegoing-concern task. The experiment required about 60 minutes to complete and waspresented in the ﬁeld via search monitor, which is menu-driven software well suited forinvestigating information acquisition because it unobtrusively and completely recordsall information acquired by the user (Biggs et al., 1993). The experiment included data from six companies that were extracted frompublic documents. Using real companies was intended to help ensure external validity,although ﬁctitious names were used. Case order was varied across participants tominimize an order effect. Each session began with a practice session to allowparticipants to become familiar with the task. Each case began with a description of the company. The second screen listed tencategories of information and then requested the selection of one category. Auditorscould select from ﬁve categories of ﬁnancial information (proﬁtability, liquidity,ﬁnancial leverage, inventory turnover, and capital intensity). Each measure had three
RAF years of data. Seven pieces of strategic information were also available (e.g. biographies10,1 of the president/CEO, senior vice presidents, and vice presidents; market demand; competition; description of products; and description of any patents) consistent with studies that have shown the importance of non-ﬁnancial information in going-concern judgments (Parker et al., 2005).36 Dependent variables and research design Rigid and adaptive behaviors pertain to speciﬁc underlying decision processes (e.g. hypothesis generation, information acquisition, and hypothesis evaluation) that result in a judgment (Hogarth, 1987). Situated cognition speciﬁcally refers to the “ongoing cognitive processes,” referred to as “sensemaking,” including environmental scanning, interpretation, and understanding, as mediating between schemas and actions (Elsbach et al., 2005, p. 424). I directly measure one tangible aspect of the ongoing cognitive process, information acquisition, as the dependent construct. Information acquisition provides important evidence of a decision maker’s schema (i.e. beliefs about the task environment including its structure). Information acquisition is the ﬁrst step in the process of learning, which relates to Holyoak’s (1991) distinction between adaptive and routine experts in that the former are able to “learn” how to adjust to changing task demands. Information acquisition can be measured in a veriﬁable and reliable way (Biggs et al., 1993), which overcomes some of the limitations to existing data collection methods for research on situated cognition (Elsbach et al., 2005). Relying on the literature, which established that the ﬁrst stage of the going-concern task is ill-structured (and the second stage is structured), I developed measures of processing behavior that are conceptually driven, conform to aspects of the task of interest, and are directly observed rather than inferred, to help improve the construct validity of the measures. While Wilner and Birnberg (1986) speciﬁcally mention verbal protocols as a source of process data, I selected computer process data because the latter have been documented to be more complete traces of information acquisition (Biggs et al., 1993). Moreover, the processing measures conform to the nature of the going-concern task, in that they represent observable aspects of rigidity and adaptation that are tailored to reﬂect the nature of the structure underlying each stage of the going-concern decision. This allows measurement of the change in processing that occurs with changes in the task (Payne and Bettman, 2004). Measures for testing H1 In their review of the literature on adaptive decision making, Payne and Bettman (2004, p. 120) summarize four possible “observable aspects of processing” that “vary with changes in the decision task”: amount of information processed, selectivity of information processed, alternative vs attribute-based processing, and attribute processing involving multiple attributes. The latter two measures, which examine attribute and alternative-based processing, have speciﬁc application in the literature; that is, they are used to identify the type of strategy selected by decision makers generally in order to be able to describe whether compensatory processing was being followed (Payne, 1976). As such, they are not relevant to the research question about “when.” In contrast, the ﬁrst two measures, amount and selectivity, are relevant to the present inquiry. It has been well established that amount of information is a valid measure of information acquisition (Ford et al., 1989; Swain and Haka, 2000) and adaptation
(Payne and Johnson, 1988; Rosman and Bedard, 1999; Rosman et al., 1994; Walsh, 1988). Auditors’While amount of information is usually a simple count, the second measure, selectivity, going-concernis related more speciﬁcally to the type of information that is examined. Below, amountand selectivity measures are developed that relate to the speciﬁc task in the experiment. judgments Because employing a single dependent measure can threaten construct validity, fourmeasures of rigidity of information acquisition are used in the ill-structured evaluationstage. “Amount ﬁnancial” compares a simple (continuous) count of the number of 37ﬁnancial informational cues that were acquired by each participant in the experiment forone company, with the similarly computed count for a second company. “Amountstrategic” is the equivalent comparison score for the qualitative cues. “Amount” measures are coarse grained since they do not inform us as to what thatinformation was. Two ﬁner grained, within-subject measures (same ﬁnancial and samestrategic), are also used. These two additional sameness measures are consistent withthe taxonomy offered by Payne and Bettman (2004) relating to measures of selectivity,and are constructed using the following three steps. In step 1, “same ﬁnancial” was constructed by determining whether a cue from one ofthe ﬁve categories of ﬁnancial information (e.g. capital intensity) was acquired for oneof the two near-bankrupt companies. If no cue was acquired, then the measure wasscored a “0”. If at least one cue was acquired, then the measure was scored a “1.” In step 2,the scores for each company were subtracted. In step 3, the absolute value of eachdifference scores was obtained and then summed over each of the ﬁve categories.Absolute values were used since the direction of a difference does not matter as describedpreviously for the “amount” scores. Rather, what matters is only that a differenceoccurred. A score of “1” implies non-rigidity or adaptation (i.e. the auditor approachedthe two companies differently, rather than automatically acquiring the same category ofinformation). For example, the auditor may have judged that a particular category ofﬁnancial information contained cues salient to one company, but not to another. To illustrate, assume that an individual acquired at least one cue in the liquidity andleverage categories for the ﬁrst company, and at least one cue in the proﬁtability,liquidity, and capital intensity categories for the second company (see Appendix 1).A score of “1” would be assigned to proﬁtability for Company 2, to liquidity for bothcompanies, to leverage for Company 1, and to capital intensity for Company 2. All othercategory/company combinations would be assigned a score of “0.” Difference scoreswould be calculated and then the absolute value would be obtained. The absolute valuescores would be summed across the ﬁve categories to produce the metric for dataanalysis. In this example, the absolute value of “1” would be scored for proﬁtability,leverage, and capital intensity, whereas liquidity and inventory turnover would receivea score of “0.” The sum of the absolute value of difference scores is three, which is thevalue used for data analysis. A similar analysis was conducted using the ﬁve categoriesof strategic information (management, market demand, competition, description ofproducts, and patents) to obtain a metric for each participant for “same strategic.”Measures for testing H2Auditors participate in the opinion stage only if they identiﬁed that they had substantialdoubt in the evaluation stage and list factors that they considered to be important inorder to determine if management might be able to mitigate the substantial doubtexpressed in the evaluation stage. These mitigating factors were then classiﬁed
RAF according to the guidance provided by the authoritative audit literature appearing in10,1 SAS 59 (AICPA, 1988, paragraphs 7-9), which provides auditors with general guidance on factors that might mitigate any doubts expressed in the initial evaluation stage. These six measures are: (1) the number of factors consistent with the guidance provided in SAS 59 (SAS 59 yes); (2) the number of factors not speciﬁcally referred to in SAS 59 (SAS 59 no);38 (3) the number of factors dealing with ﬁnancial issues (ﬁnancial); (4) the number of factors dealing with non-ﬁnancial issues (strategic); (5) the number of factors addressing past activity (past); and (6) the number of factors addressing future possible activities (future). Because auditors proceed to the opinion stage of the going-concern task and seek additional information only when they conclude in the evaluation stage that there is substantial doubt about the entity’s viability, the within-subject analysis used in the opinion stage is performed on a reduced set of participants (i.e. those who expressed substantial doubt in the evaluation stage). In contrast, the within-subject analysis used in the evaluation stage is performed for all participants. Measures in the second stage are constructed to be similar to those in the ﬁrst stage. The opinion stage measure similar to amount of information acquired in the evaluation stage is the number or amount of mitigating factors identiﬁed by participants. The opinion stage measures similar to “sameness” measures in the evaluation stage are based on the characteristics of the mitigating factors identiﬁed previously in SAS 59: ﬁnancial, strategic, past, future, consistent with or not consistent with SAS 59. The calculation of the sameness measures in the opinion stage is similar for the opinion stage (see the illustration in the Appendix 2). In sum, there are four measures of rigidity in the evaluation stage (amount and same for ﬁnancial and strategic) and seven measures of rigidity in the opinion stage. VI. Results Table I presents the within-subject results of the paired t-tests that address H1 for the ill-structured evaluation stage. H1 states that auditors will exhibit adaptive behavior in this stage of the going-concern task because the ill-structured nature of this task lends itself to adaptive behavior. Each t-test analyzes whether the difference in the means is statistically different from zero. Statistically signiﬁcant differences provide evidence of adaptive behavior. n Mean difference SE t pa Amount ﬁnancial 23 0.58 0.09 6.67 0.00 Amount strategic 23 0.64 0.09 7.00 0.00 Same ﬁnancial 23 0.93 0.13 7.38 0.00 Same strategic 23 0.83 0.11 7.47 0.00Table I.Analysis of H1 for the Notes: aNon-Bonferroni corrected p-values; tests of whether the absolute value of difference scores areill-structured stage statistically different from zero
Table I shows data across all six cases. Each of the mean differences tested in Table I is Auditors’statistically signiﬁcant at p ¼ 0.00 consistent with the expectation in H1 and providesevidence of adaptive behavior. Recognizing that the use of separate t-tests may bias the going-concernp-values in favor of signiﬁcant ﬁndings, it is appropriate to apply the Bonferroni judgmentsfamily-wise error correction to adjust p-values. Doing so, however, does not change theresults in Table I (i.e. each reported p-value remains signiﬁcant at below the 0.01 level). Table II presents the results of the t-tests in the well-structured opinion stage in cases 39where an auditor expressed substantial doubt about going-concern viability. H2 statesthat auditors will exhibit rigid behavior in this stage of the going-concern task because thestructured nature of this task promotes rigid behavior. Each t-test analyzes whether thedifference in the means is statistically different from zero. Statistically signiﬁcantdifferences provide evidence of adaptive behavior, which would not be consistent with H2. The variable in stage 2 that most closely resembles the amount variable in stage 1 isshown in Table II as “amount (no. of mitigating factors).” It is a count of mitigatingfactors. The amount variable is statistically signiﬁcant overall, which suggests adaptivebehavior that is not consistent with the expectation in H2. One of the other variables inTable II, which resembles the sameness measures in Table I, is statistically signiﬁcant(same SAS 59 yes (t ¼ 2.45 and p ¼ 0.04)) and another is marginally signiﬁcant (samestrategic (t ¼ 1.96 and p ¼ 0.08)). Otherwise, the remaining four paired t-tests are notstatistically signiﬁcant. However, a Bonferroni family-wise error correction to adjustp-values for same SAS 59 yes and same strategic would produce non-statisticallysigniﬁcant results. Therefore, after the Bonferroni correction, the p-values for only theamount (number of mitigating factors) variable would remain statistically signiﬁcant.VII. Discussion and conclusionMotivated by research on situated cognition and adaptive decision making, I investigatedthe inﬂuence of task structure on rigid and adaptive information acquisition acrossdifferent task settings in the going-concern task. To minimize threats to construct validity,I relied on the literature to establish that the ﬁrst stage of the going-concern task isill-structured (H1) and the second stage is structured (H2), employed multiple dependentmeasures. Auditors, whose professional experience interacting with and learning from othersacross both structured and ill-structured tasks, generally performed consistent withtheory. By manipulating task structure, auditors were shown to adapt to the changingtask when appropriate in ill-structured tasks. Adaptation occurs because individuals n Mean difference SE t paAmount (no. of mitigating factors) 10 0.80 0.13 6.00 0.00Same ﬁnancial 10 0.20 0.13 1.50 0.17Same strategic 10 0.30 0.15 1.96 0.08Same past 10 0.20 0.13 1.50 0.17Same future 10 0.10 0.10 1.00 0.34Same SAS 59 no 10 0.10 0.10 1.00 0.34Same SAS 59 yes 10 0.40 0.16 2.45 0.04 Table II.Notes: aNon-Bonferroni corrected p-values; tests of whether the absolute value of difference scores are Analysis of H2 for thestatistically different from zero structured stage
RAF have at their disposal the schemas necessary to apply to the novel (ill-structured)10,1 context. Auditors also remained rigid when appropriate in structured tasks when rigidity was measured by the more ﬁne-grained sameness variables, but rigidity was not observed for the course-grained count (amount) measures after the Bonferroni correction. Amount measures are course grained while the sameness measures are more ﬁne40 grained. Given the complex nature of the judgments involved in the going-concern task, it can be argued that the ﬁne-grained measures are more revealing about behavior and diagnostic regarding rigidity vs adaptation. However, it also means that research in other tasks using similar course grained and ﬁne-tuned measures should investigate whether decision behavior is adaptive across ill-structured tasks and rigid across structured tasks. If a similar pattern is observed, then researchers may be able to conclude from the accumulated evidence that rigidity exists in the underlying substance of the information acquired even if the absolute amount of information acquired differs (i.e. the decision maker is adaptive). It remains an open question as to whether the conclusions will generalize to other professionals, which is another reason to support additional research. However, task structure is a characteristic of virtually all decision contexts regardless of the domain. For instance, neurologists and CEOs make decisions in structured and ill-structured settings. Thus, the ﬁndings with auditors should hold across domains. In sum, the ﬁndings suggest the importance of explicitly accounting for the nature of the task when conducting research about decision behaviors in situated contexts. It is important to consider task structure when examining behavior because “understanding the response requires understanding the stimulus” (Gibbins and Jamal, 1993, p. 453). Although such concepts as selective perception, cognitive embeddedness, functional ﬁxedness, strategic myopia, and strategic blind spots, may have merit, they were uncovered by research designs that only implicitly accounted for task structure, and therefore may overstate the dark side of expertise. Understanding decision behavior of experts can only be enhanced: [. . .] [b]y learning what types of interactions of schema and context lead to the most effective outcomes.” Doing so shifts the focus of learning “from overly rigid models and routines that are unresponsive to change [. . .] toward routines that encourage the development of the transitory perceptual frames that are relevant to the current context (Elsbach et al., 2005, p. 431). Notes 1. The paper has described the going-concern judgment task using terminology and procedures consistent with generally accepted auditing standards (GAAS) in the USA because the participants in this study are auditors in the USA who follow these standards. A review of the comparable international standards (IFAC, 2009) describes essentially the same stages as under US standards. For example, in paragraph 16 of ISA 570, the standard discusses what the auditor should do once events or conditions are found that “may cast signiﬁcant doubt on the entity’s ability to continue as a going concern” including the evaluation of mitigating factors. That is, like US GAAS, ISA 570 speciﬁcally considers two stages, one in which substantial doubt is identiﬁed and a subsequent stage in which the event of condition that raised doubt is then investigated to see if it can be mitigated. 2. This study is interested in differences in acquisition behavior across the companies rather than in the direction of differences. Therefore, the absolute value of the difference scores is
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RAF Further reading10,1 Abdolmohammadi, M. and Wright, A. (1987), “An examination of the effects of experience and task complexity on audit judgments”, The Accounting Review, Vol. 62, pp. 1-13. Arunachalam, V. and Beck, G. (2002), “Functional ﬁxation revisited: the effects of feedback and a repeated measures design on information processing changes in response to an accounting change”, Accounting, Organizations and Society, Vol. 27 Nos 1/2, pp. 1-25.44 Cook, T.D. and Campbell, D.T. (1979), Quasi-experimentation: Design and Analysis Issues for Field Settings, Houghton Mifﬂin, Boston, MA. Spilker, B.C. and Prawitt, D.F. (1997), “Adaptive responses to time pressure: the effects of experience on tax information search behavior”, Behavioral Research in Accounting, Vol. 9, pp. 172-98. Appendix 1. Calculation of “same” ﬁnancial measure for H1 Assume that an individual acquired at least one cue in the liquidity and leverage categories for the ﬁrst company, and at least one cue in the proﬁtability, liquidity, and capital intensity categories for the second company. To calculate the “same” ﬁnancial metric, a score of “0” is assigned for each category for each company when information was not acquired and a “1” when information was acquired (step 1). In step 2, a difference score would be calculated (Companies 1 and 2). In step 3, the absolute value of the difference scores was obtained and the difference scores were summed. Here, the resulting metric used in data analysis was “3.” A similar analysis was conducted using the ﬁve categories of strategic information (management, market demand, competition, description of products, and patents) to obtain a metric for each participant for “same strategic.” Step 1 Step 2 Step 3 Category Company 1 Company 2 Difference Absolute value of difference 1. Proﬁtability 0 1 21 1 2. Liquidity 1 1 0 0 3. Financial leverage 1 0 1 1 4. Inventory turnover 0 0 0 0 5. Capital intensity 0 1 21 1Table AI. Total 3 Appendix 2. Calculation of “same” measure for H2 Assume that an individual identiﬁed at least one mitigating factor relating to a ﬁnancial cue for the ﬁrst company and at least one mitigating factor for the second company. To calculate the “same” ﬁnancial metric a score of “0” is assigned for each category for each company when information was not acquired and a “1” when information was acquired (step 1). In step 2, a difference score would be calculated (Companies 1 and 2). In step 3, the absolute value of the difference scores was obtained and the difference scores were summed. Here, the resulting metric used in data analysis was “0” indicating that there was no difference across the two companies. If the individual had identiﬁed at least one mitigating factor for either Company 1 or 2 but not both, then the difference would be 1 or 21, respectively, and the absolute value of the difference would be 1. A similar analysis was conducted for each of the remaining opinion stage variables (strategic, past, future, SAS 59 no, and SAS 59 yes). Step 1 Step 2 Step 3 Variable Company 1 Company 2 Difference Absolute value of differenceTable AII. Financial 1 1 0 0
About the author Auditors’Andrew J. Rosman is an Associate Professor at the University of Connecticut and a UniversityTeaching Fellow. He has been at the University of Connecticut since 1989 and teaches global going-concernﬁnancial reporting and analysis. Andrew J. Rosman’s primary research focus has been on how judgmentsdecision makers use accounting information with the objective of identifying ways to improvedecision behavior. Other interests include accounting regulation issues and research methods. Hehas published research in the Journal of Accounting and Economics; Journal of Accounting,Auditing and Finance; Auditing: A Journal of Theory and Practice; Academy of Management 45Journal; Journal of Behavioral Decision Making; Journal of Business Venturing; Journal of BusinessResearch; and Research in Accounting Regulation. Andrew J. Rosman can be contacted at:firstname.lastname@example.orgTo purchase reprints of this article please e-mail: email@example.comOr visit our web site for further details: www.emeraldinsight.com/reprints