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The current issue and full text archive of this journal is available at                                                www...
to which they may adapt.” Similarly, others warn that research that fails to consider the                 Auditors’environ...
RAF    a solution available from memory and for which there was no available schemata10,1   that guaranteed a solution). T...
Analyzing the demands of task environments enables researchers to examine both                      Auditors’whether and w...
RAF    reasons that the expertise of routine experts is based on mastering foundation skills, rules,10,1   and procedures ...
As one practitioner explains, “[b]ecause the assumption itself is not defined, there are            Auditors’wide-ranging i...
RAF    years of data. Seven pieces of strategic information were also available (e.g. biographies10,1   of the president/C...
(Payne and Johnson, 1988; Rosman and Bedard, 1999; Rosman et al., 1994; Walsh, 1988).               Auditors’While amount ...
RAF                      according to the guidance provided by the authoritative audit literature appearing in10,1        ...
Table I shows data across all six cases. Each of the mean differences tested in Table I is                     Auditors’st...
RAF    have at their disposal the schemas necessary to apply to the novel (ill-structured)10,1   context. Auditors also re...
used since it does not matter whether a higher or lower amount of information was acquired              Auditors’    for c...
RAF    Elsbach, K.D., Barr, P.S. and Hargadon, A.B. (2005), “Identifying situated cognition in             organizations”,...
Parker, S., Peters, G.F. and Turetsky, H.F. (2005), “Corporate governance factors and auditor            Auditors’      go...
RAF          Further reading10,1         Abdolmohammadi, M. and Wright, A. (1987), “An examination of the effects of exper...
About the author                                                                                         Auditors’Andrew J...
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55728111 auditors-going-concern

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55728111 auditors-going-concern

  1. 1. 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
  2. 2. 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
  3. 3. 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.
  4. 4. 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
  5. 5. 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.
  6. 6. 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[1]. 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
  7. 7. 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
  8. 8. (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[2]. “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
  9. 9. 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
  10. 10. 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
  11. 11. 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
  12. 12. used since it does not matter whether a higher or lower amount of information was acquired Auditors’ for company one compared with company two, but rather what matters is only that a difference occurred. A non-zero score for any of the two “amount” measures implies that the going-concern auditor demonstrated non-rigidity (flexibility) in the acquisition of information for the two judgments companies in the experiment.References 41Abdolmohammadi, M. (1999), “A comprehensive taxonomy of audit task structure, professional rank, and decision aids for behavioral research”, Behavioral Research in Accounting, Vol. 11, pp. 51-92.AICPA (1988), Statement on Auditing Standards (SAS ) No. 59: The Auditor’s Consideration of an Entity’s Ability to Continue as a Going Concern, American Institute of Certified Public Accountants, New York, NY.Anderson, J.R. (1990), The Adaptive Character of Thought, Erlbaum, Hillsdale, NJ.Ashton, R.H. (1976), “Cognitive changes induced by accounting changes: experimental evidence on the functional fixation hypothesis”, Journal of Accounting Research, Vol. 14, pp. 1-17 (Supplement).Barnes, P. and Webb, J. (1986), “Management information changes and functional fixation: some experimental evidence from the public sector”, Accounting, Organizations and Society, Vol. 11 No. 1, pp. 1-18.Beyer, J.M., Chattopadhyay, P., George, E., Glick, W.H., Olgivie, D.T. and Pugliese, D. (1997), “The selective perception of decision makers revisited”, Academy of Management Journal, Vol. 40 No. 3, pp. 716-37.Biggs, S., Rosman, A. and Sergenian, G. (1993), “Methodological issues in judgment and decision-making research: concurrent verbal protocol validity and simultaneous traces of process data”, Journal of Behavioral Decision Making, Vol. 6 No. 3, pp. 187-206.Bloom, R., Elgers, P.T. and Murray, D. (1984), “Functional fixation in product pricing: a comparison of individuals and groups”, Accounting, Organizations and Society, Vol. 9 No. 1, pp. 1-11.Bowrin, A.R. (1998), “Review and synthesis of audit structure literature”, Journal of Accounting Literature, Vol. 17, pp. 40-71.Brown, J.S., Collins, A. and Duguid, P. (1989), “Situated cognition and the culture of learning”, Educational Researcher, Vol. 18, January-February, pp. 32-42.Chang, D.L. and Birnberg, J.G. (1977), “Functional fixity in accounting research: perspective and new data”, Journal of Accounting Research, Vol. 15 No. 2, pp. 300-12.Chase, W.G. and Simon, H.A. (1973), “The mind’s eye in chess”, in Chase, W.G. (Ed.), Visual Information Processing, Academic Press, New York, NY.Chi, M.T.H., Feltovich, P. and Glaser, R. (1981), “Categorization and representation of physics problems by experts and novices”, Cognitive Science, Vol. 5 No. 2, pp. 121-52.Contu, A. and Willmott, H. (2003), “Re-embedding situatedness: the importance of power relations in learning theory”, Organization Science, Vol. 14 No. 3, pp. 283-96.Cyert, R. and March, J. (1963), A Behavioral Theory of the Firm, Prentice-Hall, Englewood Cliffs, NJ.Dearborn, D.C. and Simon, H.A. (1958), “Selective perception: a note on the departmental identifications of executives”, Sociometry, Vol. 21 No. 2, pp. 140-4.Duncker, K. (1945), “On problem solving”, Psychological Monographs 58, No. 5 (Whole No. 270), American Psychological Association, Washington, DC.
  13. 13. RAF Elsbach, K.D., Barr, P.S. and Hargadon, A.B. (2005), “Identifying situated cognition in organizations”, Organization Science, Vol. 16 No. 4, pp. 422-33.10,1 Ford, J.K., Schmitt, N., Schechtman, S.L., Hults, B.M. and Doherty, M.L. (1989), “Process tracing methods: contributions, problems, and neglected research questions”, Organizational Behavior and Human Decision Processes, Vol. 43 No. 1, pp. 75-117. Frensch, P.A. and Sternberg, R.J. (1989), “Expertise and intelligent thinking: when is it worse to42 know better?”, in Sternberg, R.J. (Ed.), Advances in the Psychology of Human Intelligence, Erlbaum, Hillsdale, NJ. Geletkanycz, M.A. and Black, S.S. (2001), “Bound by the past? Experience-based effects on commitment to the strategic status quo”, Journal of Management, Vol. 27 No. 1, pp. 3-21. Gibbins, M. and Jamal, K. (1993), “Problem-centered research and knowledge-based theory in the professional accounting setting”, Accounting, Organizations and Society, Vol. 18 No. 5, pp. 451-66. Griffin, M.M. (1995), “You can’t get there from here: situated learning, transfer, and map skills”, Contemporary Educational Psychology, Vol. 20 No. 1, pp. 65-87. Handley, K., Clark, T., Fincham, R. and Sturdy, A. (2007), “Researching situated learning: participation, identity and practices in client-consultant relationships”, Management Learning, Vol. 38 No. 2, pp. 173-91. Herbert, D.M.B. and Burt, J.S. (2004), “What do students remember? Episodic memory and the development of schematization”, Applied Cognitive Psychology, Vol. 18, pp. 77-88. Hogarth, R.M. (1987), Judgement and Choice: The Psychology of Decision, 2nd ed., Wiley, New York, NY. Holyoak, K.J. (1991), “Symbolic connectionism: toward third-generation theories of expertise”, in Ericsson, K.A. and Smith, J. (Eds), Toward a General Theory of Expertise: Prospects and Limits, Cambridge University Press, New York, NY, pp. 301-35. IFAC (2009), International Standard on Auditing (ISA) 570, “Going Concern”, International Federation of Accountants, available at: http://web.ifac.org/download/a031-2010-iaasb- handbook-isa-570.pdf (accessed April 20, 2010). Katz, R. (1982), “The influence of job and group longevities”, in Katz, R. (Ed.), Career Issues in Human Resource Management, Prentice-Hall, Englewood Cliffs, NJ. Lave, J. and Wenger, E. (1991), Situated Learning: Legitimate Peripheral Participation, Cambridge University Press, Cambridge. Libby, R. and Luft, J. (1993), “Determinants of judgment performance in accounting settings: ability, knowledge, motivation, and environment”, Accounting, Organizations and Society, Vol. 18 No. 5, pp. 425-50. Maier, N.R.F. (1931), “Reasoning in humans II. The solution of a problem and its appearance in consciousness”, Journal of Comparative Psychology, Vol. 12, pp. 181-94. Marchant, G. (1990), “Accounting changes and information processing: some further empirical evidence”, Behavioral Research in Accounting, Vol. 2, pp. 93-103. Marchant, G., Robinson, J., Anderson, U. and Schadewald, M. (1991), “Analogical transfer and expertise in legal reasoning”, Organizational Behavior and Human Decision Processes, Vol. 48 No. 2, pp. 272-90. Mason, J. and Mitroff, I. (1981), Challenging Strategic Planning Assumptions, Wiley, New York, NY. Mintzberg, H., Raisinghani, D. and Theoret, A. (1976), “The structure of ‘unstructured’ decision processes”, Administrative Science Quarterly, Vol. 21 No. 2, pp. 246-75. Newell, A. and Simon, H.A. (1972), Human Problem Solving, Prentice-Hall, Englewood Cliffs, NJ.
  14. 14. Parker, S., Peters, G.F. and Turetsky, H.F. (2005), “Corporate governance factors and auditor Auditors’ going concern assessments”, Review of Accounting and Finance, Vol. 14 No. 3, pp. 5-29. going-concernPayne, J.W. (1976), “Task complexity and contingent processing in decision-making: an information search and protocol analysis”, Organizational Behavior and Human judgments Performance, Vol. 16 No. 2, pp. 366-87.Payne, J.W. and Bettman, J.R. (2004), “The information-processing approach to decision making”, in Koehler, D.J. and Harvey, N. (Eds), Blackwell Handbook of Judgment and Decision 43 Making, Blackwell, Malden, MA, pp. 110-32.Payne, J.W., Bettman, J.R. and Johnson, E.J. (1988), “Adaptive strategy selection in decision making”, Journal of Experimental Psychology: Learning, Memory, and Cognition, Vol. 14 No. 3, pp. 534-52.Prawitt, D.F. (1995), “Staffing assignments for judgment-oriented audit tasks: the effects of structured audit technology and environment”, The Accounting Review, Vol. 70 No. 3, pp. 443-65.Reitman, W. (1965), Cognition and Thought, Wiley, New York, NY.Rosman, A. and Bedard, J. (1999), “Lenders’ decision strategies and loan structure decisions”, Journal of Business Research, Vol. 46 No. 1, pp. 83-94.Rosman, A., Lubatkin, M. and O’Neill, H. (1994), “Rigidity in decision behaviors: a within-subject test of information acquisition using strategic and financial informational cues”, Academy of Management Journal, Vol. 37 No. 4, pp. 1017-33.Rosman, A., Seol, I. and Biggs, S. (1999), “The effect of stage of development and financial health on auditor decision behavior in the going-concern task”, Auditing: A Journal of Practice & Theory, Vol. 18 No. 1, pp. 37-54.Shanteau, J. (1992), “The psychology of experts: an alternative view”, in Wright, G. and Bolger, F. (Eds), Expertise and Decision Support, Plenum, New York, NY, pp. 11-23.Simon, H.A. (1981), The Sciences of the Artificial, 2nd revised and enl. ed., MIT Press, Cambridge, MA.Staw, B.M. (1981), “The escalation of commitment to a course of action”, Academy of Management Review, Vol. 6 No. 4, pp. 577-87.Stewart, T.R., Roebber, P.J. and Bosart, L.F. (1997), “The importance of the task in analyzing expert judgment”, Organizational Behavior and Human Decision Processes, Vol. 69 No. 3, pp. 205-19.Swain, M.R. and Haka, S.F. (2000), “Effects of information load on capital budgeting decisions”, Behavioral Research in Accounting, Vol. 12, pp. 171-98.Turner, B.A. (1976), “The organizational and interorganizational development of disasters”, Administrative Science Quarterly, Vol. 21 No. 3, pp. 378-97.Venuti, E.K. (2004), “The going-concern assumption revisited: assessing a company’s future viability”, CPA Journal, Vol. 74 No. 5, pp. 40-3.Walsh, J.P. (1988), “Selectivity and selective perception: an investigation of decision makers’ belief structures and information processing”, Academy of Management Journal, Vol. 31 No. 4, pp. 873-96.Wilner, N. and Birnberg, J. (1986), “Methodological problems in functional fixation research: criticism and suggestions”, Accounting, Organizations and Society, Vol. 11, pp. 71-80.
  15. 15. 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
  16. 16. 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:andy.rosman@business.uconn.eduTo purchase reprints of this article please e-mail: reprints@emeraldinsight.comOr visit our web site for further details: www.emeraldinsight.com/reprints

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