Printer Friendly

An investigation of the attributes of top industry audit specialists.

ABSTRACT: Prior research in psychology and auditing has established that in addition to ability, experience, and knowledge, many other attributes such as confidence and communication skills are also important to expertise. The literature also suggests that the importance of various expert attributes differ by professional rank. This study extends this literature by providing evidence on an expanded list of attributes of top industry audit specialists (TIASs). Specifically, the study elicited data from 114 senior audit partners known to be TIASs by a Big 6 accounting firm. These subjects generated an extensive list of attributes of TIASs in an open-ended questionnaire and assessed their importance. They also assessed the importance of each attribute in a 25-item predefined list. Our findings confirm the importance of many attributes reported in the expertise literature that can be classified as judgment/technical expertise. Our study also identifies detailed attributes related to characteristics that can generally be classified as personality and social attributes. For example, we present evidence on the importance of many attributes that can be classified as leadership (e.g., "respected"), marketing (e.g., "marketing focus"), and accepted-as-authority (e.g., "recognition") characteristics. The findings are robust and applicable to various industry specializations. Implications for research and practice are discussed.

Keywords: audit specialty; expert attributes; industry audit specialists.

Data Availability: Contact the first author.

INTRODUCTION

Recent literature (e.g., Abdolmohammadi and Shanteau 1992; Libby and Tan 1994; Tan and Libby 1997; Tan 1999) has identified many determinants of expertise in auditing. A conclusion from this literature is that in addition to well-known attributes such as ability, knowledge, and experience, traits such as confidence, leadership, and communication skills are also important to expertise. For example, Tan and Libby (1997) found that tacit managerial knowledge (i.e., leadership attributes such as management of self, others, and career) is important for superior performance at the manager level. The authors also found that while technical skills are important for superior performance at the staff level, problem-solving abilities distinguish superior performance at the senior rank. Thus, in general, while judgment/technical skills are more important for superior performance at the lower professional ranks, personality/social attributes such as tacit managerial knowledge are more important for superior performance at higher professional ranks.

This paper's primary objective is to contribute to the understanding of the determinants of expertise by investigating attributes of expertise deemed important by senior partners who had been designated by their firm as top industry audit specialists (TIASs). The insights of these individuals who have achieved such status, and who assist in the identification and development of those who may achieve such status at some point in their careers, can contribute to a better understanding of the determinants of top experts. Our knowledge in this area is still limited (cf. Bedard 1989; Bedard and Chi 1993; Tan and Libby 1997) and no prior study has addressed the TIASs' attributes. However, based on Tan and Libby's (1997) results about the importance of tacit managerial knowledge for superior performance at the manager level, we expect to find that in addition to judgment/technical skills, many personality/social attributes that include tacit managerial knowledge are important for performing at the TIAS level.

A secondary objective of the paper is to provide evidence on the differences in expertise attributes by industry of specialization. This objective is important because many accounting firms have made changes to their organizational structure and training programs around industry specialization to enhance industry-specific expertise (Emerson 1993; Solomon et al. 1999; Wright and Wright 1997). However, previous studies have not reported data on possible differences in expertise attribute importance between industries of specialization. Understanding detailed expert attributes, and whether such attributes vary across industries, can assist accounting firms in designing decision aids, building expert systems, developing training programs, specifying guidelines for hiring, and establishing procedures for employee evaluation/promotion (Abdolmohammadi and Shanteau 1992) (hereafter A&S).

Our research extends A&S, who used a sample of auditing students and auditors of varying ranks, but could not independently classify any of their subjects as experts or specialists. Furthermore, there were major unexplained differences between the subjects concerning their importance ratings of the attributes. For example, A&S (1992, 169) reported that, "The top group of managers/ partners tended to place more emphasis on how an expert thinks than on how an expert behaves. In contrast, the middle group of senior/supervisors tended to put more importance on decision-making strategy. Finally, students stressed externally identifiable attributes." We limited our participants to TIASs. (1) These professionals are highly desirable for studying expert attributes at the detailed level because having advanced to the highest levels of specialty in their fields, they have insights about expert attributes that may be lacking in A&S's subjects. Scholars with extensive practice experience (e.g., Graham 1993) have observed that for the study of expertise in auditing, researchers should use recognized experts in the profession.

In comparison with A&S, we investigate an expanded list of expert attributes that are also more auditing-specific. Our primary focus is on a list of attributes that TIASs generated from an open-ended questionnaire. However, we also asked the TIASs to assess the importance of 25 predefined attributes that included five distracter items. In comparison, A&S used a list of 20 attributes that included seven distracter items.

The remainder of the paper is organized as follows: the following section provides a review of the background literature as a means of developing the study's research questions. The research method and data analysis are presented in the subsequent two sections, followed by a summary of the findings and several conclusions in the final section.

BACKGROUND

Definition of Expertise

The study of expertise has been a topic of interest to professionals and researchers for many decades. Shanteau and Stewart (1992) reference studies of experts that date back to 1917. (2) In general, this early research indicates that experts within their specialties are skilled, competent, and think in qualitatively different ways than novices (Anderson 1981; Chi et al. 1988). Auditing research has generated results that are consistent with this conclusion. For example, Salterio (1994) reported archival observations leading to a conclusion that definite improvements in efficiency and effectiveness are achieved as experienced auditors (i.e., managers) move higher in "expertise" levels at their firms' central research units. (3) More recently, Wright (2001) reported that financial institution auditors' judgment performance improves by achieving higher levels of expertise defined as attaining higher levels of professional ranks. Ballou (2001) reported that experienced auditors who had high confidence in their review ability were less likely to require preparers to perform extensive follow-up procedures unless necessary. This result implies that confidence (as an attribute of expertise) has a significant impact on auditor judgments.

Despite this long history of research, it is difficult to find a broadly accepted definition of expertise in the literature because there is no widely accepted theory of expertise (Alba and Hutchinson 1987). (4) The definition of expertise in the auditing literature is also varied. An early view was that expertise is the possession of a large body of knowledge and procedural skills that are reflected in years of audit experience (Bedard 1989). Other studies (e.g., Bedard and Chi 1993; Davis and Solomon 1989; Libby 1995) have adopted a performance-based concept of expertise defined in terms of the efficiency and effectiveness with which auditors perform their tasks. For example, task-specific formulations of several variants of superior performance such as ability, knowledge, and experience have been reported in the auditing literature (cf., Bonner and Lewis 1990; Libby 1995; Libby and Luft 1993; Libby and Tan 1994).

In practice, expertise is often reflected by official recognition (e.g., attainment of the partner rank) and consensual acclamation (e.g., peer recognition of one's top industry specialization). For example, in developing ExperTAX, a tax-planning expert system, Coopers & Lybrand (a predecessor firm of PricewaterhouseCoopers) used tax-planning professionals within the firm who were recognized by the firm as experts in that field (Shpilberg et al. 1986). This practical method of expert identification provides a useful vehicle for investigating various expert attributes and has been suggested by researchers in recent years (cf. A&S; Tan 1999; Tan and Libby 1997). Our focus is on detailed attributes of TIASs and, as such, our results may not be generalizable to other professionals who are not designated as experts.

The results reported in the literature (e.g., A&S, Tan and Libby 1997; Tan 1999) indicate that in addition to knowledge, ability, and experience, attributes such as confidence, communication, and leadership skills are also important to auditing expertise. What is not clear is the nature and extent of the importance of various attributes to specific levels of expertise such as TIASs. Consequently, we focus our investigation on an exploratory research question as a means of providing evidence on this issue.

R[Q.sub.1]:: What are the attributes of expertise, and their perceived relative importance, for top industry audit specialists?

A related issue is whether the importance of various expert attributes varies by industry of specialization. This issue is important because it has implications for the training and assignment of industry specialists. One might expect that attribute ratings by TIASs will vary by industry. For example, experience and depth of technical skills may be more important to industries with higher litigation risk (e.g., high-tech) than those with lower risk of litigation (e.g., utilities): If these differences are found, then they imply a need for differential training of industry specialists where they are provided more training on differential attributes for their industries.

Maletta and Wright (1996) argued that companies in a regulated industry are likely to have stronger internal control structures than companies in an unregulated industry. Consequently, the authors predicted and found that companies in a regulated industry have fewer incidences of financial statement errors than companies in an unregulated industry. In addition, the companies in the regulated industries "had proportionately more non-routine detected errors than routine errors" (Maletta and Wright 1996, 80) than companies in the unregulated industries. Based on these results, one might expect that auditors practicing in various industries have varying levels of importance placed on different expert attributes.

An alternative assertion might be that attribute ratings by TIASs are relatively similar across industries, at least for some of the expert attributes. For example, personality/social attributes may be equally important for TIASs practicing in various industries. For these attributes, core training could be provided to all specialists regardless of their industry of specialization. Consequently, we establish an exploratory research question that addresses whether the relative importance of various expert characteristics is similar across industry and regulatory classifications.

R[Q.sub.2]: Does the importance of various expert attributes differ across TIASs with different industry specializations?

Industries can be classified at different levels. At the detailed level, one can focus on specific industry specializations such as banking or high-technology manufacturing. A more global classification might be regulated/unregulated. As discussed later, for the purpose of data analysis we classify industry into seven categories and also into regulated versus unregulated.

RESEARCH METHOD

Detailed Expert Attributes

A&S used an open-ended questionnaire and a predefined list of attributes to collect data from their subjects. We substantially revised these questionnaires for use in our study. First A&S used an open-ended questionnaire in which the subjects were asked to list as many attributes as they deemed important for describing one as an expert, broadly defined. They did not ask the subjects to assess the degree of importance of these attributes. We used a similar questionnaire to elicit data from our TIASs. However, we asked our subjects to specifically list attributes that they deemed to be important for "top industry specialists." We also asked them to assess the importance of each listed attribute.

Second, A&S used a list of 20 generic expert attributes, including seven distracter items. We expanded the list to 25 and reduced the number of distracter items to five; thus, we included seven additional attributes over A&S's list. The sources of these seven attributes were either the A&S results from their open-ended listing of attributes ("intelligence," "quick thinker"), or evidence from auditing research indicating their importance to auditing expertise. Specifically, "configural processing" was adopted from Brown and Solomon (1991), "feedback" from Hirst and Luckett (1992), "pattern recognition" from Bedard and Biggs (1991), "problem solver" from Bonner and Lewis (1990), and "research skills" from Salterio (1994). Finally, we included "task analysis" as an attribute that makes identification of the nature of the task (e.g., complex versus simple; unique versus repetitive) an important attribute of expert auditors. (6) However, following A&S, the focus of the predefined attributes was on expertise, not leadership, marketing, or other personality/social attributes. These attributes were expected to be identified by the TIASs in the open-ended questionnaire.

A&S's predefined attributes are presented in the Appendix in alphabetical order in Column 1, where expert attributes and distracter items are identified. Also identified in the Appendix are the overall importance ratings from A&S in Column 2 and the descriptions of the attributes in Column 4. Column 3 in the Appendix presents the list of 25 attributes in our study, including five distracter items. For the 13 attributes that we adopted from A&S, the descriptions provided in this study were familiar. We developed the descriptions of the other seven attributes based on the nature of the attributes as described in the sources where we adopted them.

Participants

Responses were obtained from 114 of a population of 145 target senior audit partners (a 79 percent response rate) in early 1990s from many offices of a Big 6 accounting firm. (7) The sample represents the senior partners recognized by the firm as TIASs. The participating firm used a formal process to evaluate and place patterns in this category. This formal process provided a practical means of identifying TIASs for our research purposes. The predominant criteria used by the firm for classifying senior auditors as TIASs at the time of this study included the following (not in order of importance):

* The number of articles published in industry publications

* Demand for presentations/speeches on industry issues

* Service on firm and industry association national committees

* Service on AICPA industry committees

* Years of experience in serving clients in the industry

* Sought out for technical support/advice on industry issues by others in the firm and the industry

Table 1 presents demographic information about this sample. (8) As reported in Panel A, the sample's overall audit experience averaged 21.57 years with a standard deviation of 6.40 and a range of 10-40 years. The average experience in audit specialty was 18.40 years with a 7.38 standard deviation and a range of 4-40 years. In addition, while 81 (71 percent) of the TIASs in the sample held only bachelor's degrees, the remaining 33 (29 percent) also had graduate degrees.

Panel B in Table 1 presents information on TIASs specialization by industry group and by the nature of the industry. Eighteen TIASs specialized in consumer products or retail industries such as apparel or food. We classified these industries as unregulated. Other industries classified as unregulated were manufacturing (n = 18) and real estate/construction (n = 10). The remaining industries were classified as regulated. They include financial services (n = 32), government/public service (n = 15), utilities (n = 10), and healthcare (n = 11).

The Questionnaire Packet

The questionnaire packet had three parts, each of which was included in a separate sealed envelope. Part one was a demographic questionnaire. It was attached to a cover letter and the participants were instructed to complete it first and enclose it in a large return envelope. Part two was an open-ended questionnaire requesting that the participants list as many attributes as they deemed relevant to describe a "top industry specialist" and rank these attributes using a 1-5 Likert scale, where 1 = minimally important, 2 = mildly important, 3 = moderately important, 4 = very important, and 5 = extremely important. Upon completion of this part, the participants were instructed to enclose their responses in a separate, sealed envelope to be included in the large return envelope.

Part three listed the 25 pre-defined attributes in the Appendix along with their definitions. The participants were instructed to rank the attributes by their level of importance for "top industry specialist" using the 1-5 Likert scale as specified above in a forced categorical ranking procedure. (9)

DATA ANALYSIS

Open-Ended Attributes

Participants listed on average 10.35 attributes with a standard deviation of 2.25 (range: 4-14). We transcribed these open-ended attributes directly from participant responses into a list of 89 attributes, where only clearly similar words or phrases (e.g., confidence and self-confidence) were interpreted as similar. (10) We did not subjectively interpret and combine any word into any attribute or category. However, most of the 89 attributes were listed by only a handful of participants and, thus, did not provide sufficient data points for analysis. For example, one subject had listed "charismatic" as an attribute of a TIAS. Similarly, two participants' listed "administrative abilities," three listed "similar background," while four listed "politically aware." Two attributes, "well-rounded/global" and "innovative" were listed by 10 TIASs.

To focus on the attributes that were listed by a significant number of TIASs, we employed a 10 percent cut-off point. As used by A&S, this criterion assumes that if an attribute is not listed by at least 10 percent of the participants in all industries, then it is a less well-known or important attribute. This procedure resulted in accepting 32 attributes that were identified by at least 11 of the 114 participants. These attributes are listed in the first column in Table 2. Attributes annotated with an a identify the attributes that were listed by 10 percent of the subjects in A&S' study. Examples of specific terms or phrases used by TIASs are provided in Column 2 of Table 2, followed by the number of TIASs who selected the attribute, the mean, the median, the standard deviation, a weighted score, and rank. The weighted score is based on the number of listings of an attribute times the importance rating given to the attribute by each of the TIASs. This weighted score was used to develop the overall importance rank of the 32 attributes.

The mean of the 32 attributes listed in Table 2 range from 3.93 for "analytical skills" to 4.81 for "knowledge." Since the importance rating scale was 1-5, these data indicate that all 32 attributes in Table 2 were viewed by TIASs as very important (median = 4) to extremely important (median = 5). The prevalence of 4 and 5 ratings was expected because the subjects were asked to list only those attributes that they believed to be important. Nevertheless, the means and medians reported in Columns 4 and 5, respectively, indicate that some attributes are rated as more important than others. For example, the first-ranked attribute, "knowledge" was selected by 63 TIASs with a mean importance rating of 4.81 and a median of 5. While approximately the same number of TIASs identified "communication" as an important attribute, its mean rating was 4.40 and its median rating was 4.5.

It is important to emphasize that some of the attributes listed in Table 2 could be classified into other attributes. For example, the 27th ranked "good listener" and the 29th ranked "public speaking" might be included with the second ranked attribute, "communication." Similarly, the 15th ranked "current" could be included in the first ranked attribute, "knowledge." We avoided introducing subjectivity in coding and also avoided double counting in situations where a subject listed, for example, both public speaking and good listener as important attributes.

In comparison to the 32 attributes in our study, A&S's study listed 22 attributes that were identified by at least 10 percent of their subjects. As marked in Table 2, our list of attributes contains most but not all of the attributes that A&S identified. Specifically, "decisive," "objective," "looks at alternatives," "perceptive," "fair," "informal," "judgment," and "organized" from A&S are not in Table 2, but most of these attributes were identified by only 10 percent of A&S's subjects. On the other hand, 19 attributes identified by at least 10 percent of our TIAS participants were not identified by at least 10 percent of A&S's participants. For example, from the top ten attributes in Table 2, only six were identified by A&S. The four missing attributes are "technical skills," "leadership," "recognition," and "people skills." Importantly, "technical skills" have been reported to be more important for lower-rank auditors (see for example, Tan 1999), but our result indicates that TIASs view it as very important (median = 4) for top specialists. The remaining three attributes can be viewed as leadership characteristics that are more important at the top audit ranks. These are similar to what Tan and Libby (1997) report as tacit managerial knowledge. The next ten attributes in Table 2 can broadly be classified as leadership (e.g., "respected"), marketing (e.g., "marketing focus") and accepted-as-authority (e.g., "recognition") in one's area of specialization.

In summary, in addition to expert characteristics that were identified by the subjects in A&S study, our TIASs listed many attributes that can generally be classified as leadership, marketing, and being accepted as an authority in the field. While other authors (e.g., Bhamornsiri and Guinn 1991; Emby and Etherington 1996; Tan and Libby 1997; Tan 1999) have recognized the importance of leadership and marketing skills for high-level professional ranks, identification of the detailed attributes within these skills and the authority attributes in this study provide new evidence to answer R[Q.sub.1]. Specifically, the TIASs clearly recognized the importance of knowledge and experience, which have been identified as critical factors in other studies (e.g., A&S; Tan and Libby 1997). However, a knowledgeable, experienced person may not be able to be an effective expert without possessing attributes such as leadership, marketing, and authority skills.

Pre-Defined Attributes for TIASs

The pre-defined attributes also provide evidence to respond to R[Q.sub.1]. The importance ratings of the pre-defined attributes are presented in Table 3. The list of attributes and the importance of each of these attributes to TIASs are provided in a descending order from the most to the least important. To facilitate the understanding of the attributes, we present them in five levels by their importance ratings of "extremely important," "very important," "moderately important," "mildly important," and "minimally important." The cut-off points are determined by significant differences based on the non-parametric Mann-Whitney test. In addition, Table 3 identifies the attributes that are directly comparable between the open-ended and the pre-defined groups with an a.

As shown in Table 3, "current knowledge" was ranked number 1 and was the only attribute assigned to the "extremely important" category. The rating for "current knowledge" was significantly higher than the rating for the next highest rated attribute, "problem solver," and by extension all other attributes (Mann-Whitney W-Statistic = 11,700.5, p = .0025). A significant break next occurs between the 11th and 12th ranked attributes, "quick thinker" and "inquisitive," where the Mann-Whitney test shows a significant difference between these two attributes (W-statistic = 12,047.5, p = .0369). The next break is between 20th and 21st ranked attributes, "research skills" and "pattern recognition," where the mean rating drops to 1.98; thus, the attribute can be classified as "mildly important" (W-statistic = 11,576.5, p = .002). Finally, there is a break in importance rating between the 23rd and 24th ranked attributes (W-statistic = 15,541.5, p = .000). Thus, the last two attributes are assigned to the category of "minimally important."

As expected, distracter items ranked low in importance ratings. Specifically, while "assertive" and "energetic" ranked 18th and 19th, "perfectionist" and "methodical" ranked dead last (i.e., 24th and 25th). However, the final distracter item, "inquisitive" ranked 12th in importance.

Comparison between Open-Ended and Pre-Defined Attributes

Since all of the open-ended attributes in Table 2 are rated in the 4-5 (extremely important to very important) range, they are directly comparable to the top two groups of attributes in Table 3. This comparison indicates that nine of the top 11 attributes are the same. "Perceptive" and "knows what is relevant" are the two attributes that were not listed by 10 percent of the TIASs in the open-ended questionnaire. However, these two attributes can broadly be classified, respectively as "analytical skills" and "knowledge" that are in Table 2. The 20th ranked attribute in Table 3 would also seem to be contained as the third ranked attribute in Table 2, "technical skills." As stated earlier, "inquisitive," which was meant to be a distracter item in the pre-defined attributes, was nevertheless ranked 12th in importance, receiving a mean rating of 3.16. "Inquisitive" was also identified by the TIASs as an important attribute in the open-ended questionnaire (see the 22nd ranked attribute in Table 2). Thus, setting other distracter items aside, only "stress tolerance" and "feedback" in Table 3 cannot be easily identified with the open-ended attributes in Table 2.

In summary, both the open-ended and the pre-defined attributes provide consistent evidence to R[Q.sub.1] However, as stated earlier, the open-ended results provide a richer list of attributes, particularly in leadership, marketing, and authority areas that were not included in the list of predefined attributes.

Effects of Industry Specialization

To answer R[Q.sub.2], we analyzed the open-ended and pre-defined attributes for the effects of industry specialization on their importance ratings. We analyzed the data at two levels of industry specialization per Table 1. The first level was the seven-category industry specialization and the second level was the regulated versus unregulated industries. The nonparametric Kruskal-Wallis test was used for analysis. Since all TIASs had rated every pre-defined attribute, we had enough data points for analysis of all pre-defined attributes. However, for the open-ended attributes, only those that had received at least 20 listings provided the minimum data points for analysis. This criterion resulted in 17 attributes being included in data analysis.

The numerous Kruskal-Wallis tests that were performed resulted in very few significant effects of industry specialization on attribute importance. For example, the 17 Kruskal-Wallis tests of the seven-level industry effects of the 17 selected open-ended attributes resulted in only one marginal (H-statistic = 11.58, p = 0.07) effect for "creativity." This significant result might be expected by chance. However, the Kruskal-Wallis test of this attribute at the regulated versus unregulated industry level also resulted in a marginally significant effect (H-statistic = 3.59, p = 0.06) indicating that "creativity" is more important for unregulated industries (mean rating = 4.56) than regulated industries (mean rating = 4.15). In addition, marginal industry effects were observed for "recognition" (H-statistic = 3.23, p = 0.07) and "people skills" (H-statistic = 3.63, p = 0.06) at the regulated versus unregulated industry level. In both cases, the TIASs indicated that these attributes were more important for unregulated industry specializations than regulated.

Similarly, a battery of 50 Kruskal-Wallis tests of the 25 pre-defined attributes at the seven and two industry levels resulted in only a few significant effects. The only significant result at conventional levels was for "experience," where the TIASs rated it as more important (H-statistic = 5.60, p = 0.02) for unregulated industries (mean = 4.54) as compared with regulated industries (mean = 3.97). "Perceptive" was the only attribute that was marginally significant (H-statistic = 11.10, p = 0.09) for the seven-level industry analysis. It was not significant in the analysis at the regulated versus unregulated industry level. Additionally "feedback" (H-statistic = 3.43, p = 0.06) and "problem selection" (H-statistic = 2.72, p = 0.10) were found to be marginally more important for regulated than unregulated industries.

Overall, with the exceptions noted above, the industry-effect results do not show widespread differences for the importance of expert attributes between various industries. These results lead us to conclude that the attribute importance ratings are robust and that they generally apply equally to various industries of specialization.

SUMMARY AND CONCLUSIONS

Two approaches were used in this study to elicit attributes of top industry audit specialists (TIASs) from a large sample of senior partners designated by their then Big 6 accounting firm as TIASs: an open-ended questionnaire and an evaluation of attributes in a pre-defined list. Consistent with prior auditing research (e.g., Abdolmohammadi and Shanteau 1992 [A&S]; Tan 1999), the results from both approaches indicate the importance of "experience," "knowledge," and "ability" to auditing expertise. In fact, the top 11 attributes classified as "extremely important" or "very important" in the pre-defined list were directly (nine attributes) or by extension (the remaining two attributes) comparable with those listed by at least 10 percent of the TIASs in the open-ended attributes. This convergence confirms the importance of these attributes for TIASs. However, we found many additional attributes such as "recognition" and "respected" that were listed by at least 10 percent of the TIASs in the open-ended attributes. These attributes can be classified into categories such as leadership (e.g., respected), marketing (e.g., "marketing focus"), and accepted-as-authority (e.g., recognition"). While the leadership attributes can be viewed as similar to those that Tan and Libby (1997) call tacit managerial knowledge, other categories provide a list of detailed attributes that are worthy of further investigation for their effects on expert performance in future research.

A related observation from the open-ended attributes was that many of the attributes related to interpersonal skills that cover the relationships between TIASs and their clients, colleagues, and subordinates. These tacit managerial knowledge attributes are "leadership," "communication skills," "good team member," "good listener," and "public speaking." Although the literature on leadership is rich with studies of interpersonal skills and their effects on colleagues and subordinate behavior, very little auditing research has been reported on these attributes. For example, Pasewark et al. (1993) provided evidence that interpersonal skills enhance audit efficiency.

The 25 pre-defined attributes included five distracter items. While, as expected, four of these attributes scored low or very low, the fifth one, "inquisitive" was ranked 12th and was also identified in the open-ended questionnaire as an important attribute. This evidence indicates that "inquisitive" should be included in future studies of expert attributes in auditing. Being curious about all aspects of an auditing problem (part of the definition provided for "inquisitive") and the eagerness of the auditor to collect evidence to investigate sensitive issues may have led TIASs to assess this attribute higher than we anticipated for a distracter item. Thus, "inquisitive" may have been rated highly perhaps as a research skill and, thus, it should be included in future studies of expertise in auditing.

The participants in this study were highly experienced senior partners and were all classified as top industry audit specialists by their firm. However, we did not have access to the personnel data needed for investigating the relationship between the formal criteria that the accounting firm used to identify these partners as top industry audit specialists and the importance ratings of the attributes. Such an investigation is worthy of pursuit in future research. Also, since our data were collected from a single firm, future research should consider a cross section of firms to investigate possible firm effects.

A related issue is that the TIASs in our study generated a significant number of attributes that were not identified in prior literature. For example, of the 32 attributes listed by 10 percent of TIASs and shown in Table 2, only 13 were identified by 10 percent of A&S's subjects, including managers and partners. Tan (1999), who replicated A&S, studied 20 attributes in which he added some attributes that Tan and Libby (1997) call tacit managerial knowledge. These attributes included "business acumen," "client knowledge," "drive," "interpersonal skills," "leadership" "teamwork," and "visibility." The differences between our study and those in prior literature may raise questions about the stability of the research findings regarding attributes of expertise or differences in participants or changes across time. However, we note the highly specialized and experienced nature of our subject group and believe that the additional attributes generated by this group are worthy of inclusion in future experimental research on auditing expertise. This is particularly a promising research avenue because our study was conducted before the business scandals and audit failures such as Enron and WorldCom. These events and the legislation of the Sarbanes-Oxley Act of 2002 (U.S. Congress 2002) may have changed the nature of TIASs' attributes.

We analyzed the data for the effects of TIASs' industry specialization on the importance ratings of both open-ended and pre-defined attributes. Only a few attributes indicated industry effects at marginal or significant levels and there was no clear pattern for these results either. Nevertheless, researchers should consider investigating the few significant effects. For example, the fact that there is a difference in the extent of importance of experience between regulated and unregulated industries could be explained in two ways. One, the difference was idiosyncratic to the respondents, and not a reflection of industry differences. Two, the difference could be the result of the fact that regulated industries often have clearly specified regulatory requirements that must be met, and some of the industry specific audit issues are nonjudgmental in nature, requiring less experience in the industry.

By providing the detailed expert attributes in this study, our results may assist accounting firms in their efforts to foster the development of TIASs. By knowing the attributes that are important for TIASs, accounting firms can develop decision aids that reinforce such attributes. For example, decision aids that provide research tools will be helpful for working as a TIAS where the possession of research skills is viewed as an important expert attribute. Likewise, knowledge of the detailed attributes could be used to improve training programs to assist professionals to become TIASs so that these attributes can be developed and assessed. The assessment of these attributes can also be helpful for hiring and promotion of auditors.

The study had several limitations that signal opportunities for future research. One limitation was that we did not have a general theory of top industry audit specialist attributes to guide us as to which attributes to include or exclude in our pre-defined list. Knowledge of the issue is presently incomplete to offer such a theory and our results indicate that this is a legitimate concern. In fact, of the basic expectations we made, some were borne out by the data and some were not. For example, while our expectation about four of the distracter items (e.g., methodical) being assessed low in importance was supported, the fifth attribute, "inquisitive" was not. Furthermore, the open-ended results indicated that many attributes (e.g., attributes that can be classified as being accepted-as-authority) not included in our pre-defined list were perceived to be important by our sample. This finding indicates that our knowledge of expert attributes is expanding as we learned about the relevance of new attributes in this study. Thus, a research direction is to develop a general theory of top specialist attributes.

Finally, the ratings of TIASs in our study do not provide direct evidence of a link between expert attributes and superior performance. Future empirical research is needed to investigate this link in audit settings. In particular, the identification of many interpersonal skills, leadership, marketing, and accepted-as-authority attributes in this study indicate that the effects of these attributes on expert performance should be investigated in future research. This research direction is potentially useful because the importance of these attributes was mostly documented in the open-ended questionnaire. These attributes signal the importance of a broader level of performance (e.g., ability to attract and keep clients) than just technical auditing performance.
APPENDIX
PRE-DEFINED ATTRIBUTES

A&S [1992] Study
             (1)         (2)
         Attribute       Rank

 1   Adaptability          4

 2   Assumes               2
     Responsibility

 3   Communicates         10
     Expertise

 4   Creativity           11

 5   Current               3

 6   Decisiveness          7
     [dis]

 7   Energetic            15 *
     [dis]

 8   Experience            6

 9   Inquisitive          12
     [dis]

10   Knows What is         1
     Relevant

11   Makes                14
     Exceptions

12   Methodical           17
     [dis]

13   Perceptive            5

14   Perfectionist        18 **
     [dis]

15   Physical             20
     Appearance
     [dis]

16   Problem              15 *
     Selection

17   Problem              13
     Simplification

18   Self-Confidence       8

19   Stress Tolerance      9

20   Warm and             18 **
     Friendly [dis]

        This Study
            (3)                          (4)
         Attribute                    Description

 1   Adaptability        Adjusts decision-making strategy to fit
                         current situation. Is responsive to changes
                         in conditions of the on-going problem
                         situation.

 2   Assertive [dis]     Is insistent on stating the goals and
                         objectives. Makes decisions quickly and
                         emphatically based on clear objectives.

 3   Assumes             Accepts responsibility for the outcomes of
     Responsibility      decisions, successful or unsuccessful. Is
                         willing to stand behind his/her decisions.

 4   Communicates        Convinces others that he/she has specialized
     Expertise           knowledge. Effectively communicates his/her
                         ability to make decisions to others.

 5   Configural          Processes information in interaction with
     Processing          other pieces of information. Recognizes the
                         interrelationships between various types of
                         evidence.

     Not included

 6   Current             Has an extensive knowledge base. Makes a
     Knowledge           special effort to keep up with facts, trends,
     Replaced with       and developments.
     Assertive

 7   Energetic [dis]     Invests large amounts of energy into problem
                         solving. Often spends extra time and energy in
                         making decisions.

 8   Experience          Effectively uses direct and indirect
                         experience to make decisions. Is skillful in
                         making decisions based on past experience.

 9   Feedback            Uses available information and decision aids
                         to assess the accuracy of his or her
                         decisions. Makes problems less complex by
                         seeking appropriate feedback and decision
                         aids.

10   Inquisitive [dis]   Exhibits high degree of inquisitiveness in
                         problem-solving situations. Is curious about
                         all aspects of the issue.

11   Intelligence        Has a high level of intelligence. Understands
                         complex problem situations quickly.

12   Knows What is       Readily distinguishes relevant from irrelevant
     Relevant            information in a problem. Utilizes only what
                         is relevant; ignores what is irrelevant.

13   Makes               Knows when to follow established decision
     Exceptions          strategies and when not to. Doesn't have just
                         one way to solve problems.

14   Methodical          Approaches new problem situations with only
     [dis]               one thought-out plan. Always proceeds in the
                         same step-by-step way to make decisions.

15   Pattern             Readily recognizes the pattern of errors in a
     Recognition         body of collected evidence. Proceeds to make a
                         judgment based on the recognized pattern.

 16  Perceptive          Is able to extract information from a problem
                         that others cannot see. Is insightful in
                         recognition and evaluation of a confusing
                         situation.

 17  Perfectionist       Attempts to achieve perfection by always
     [dis]               seeking the best of all possible strategies.
                         Persists on working to find the absolute best
                         solution for the problem.

     Not included

 18  Problem             Uses foresight and planning in selecting which
     Selection           problems to work on. Tackles only those
                         problems that he/she can effectively handle or
                         resolve.

 19  Problem             With complex problems, knows how to use a
     Simplification      divide-and-conquer approach. Works on parts to
                         get a better understanding of a complex
                         problem.

 20  Problem Solver      Is capable of generating new approaches to
                         solving difficult problems. When faced with a
                         new problem, he/she can develop new strategies
                         to solve that problem.

 21  Quick Thinker       Quickly perceives data relationships. Is able
                         to rapidly envision future possibilities and
                         pitfalls.

 22  Research Skills     Has the ability to seek out new sources of
                         information to help tackle difficult problems.
                         When information is not available, he/she can
                         develop methods for obtaining what is needed.

 23  Self-Confidence     Has strong belief in his/her ability to make
                         good decisions. Is calm and self-assured while
                         making decisions.

 24  Stress              Is able to make decisions under high-stress
     Tolerance           situations. Continues to be an effective
                         problem solver even as conditions
                         progressively worsen.

     Not Included

 25  Task Analysis       Identifies the nature of the task easily. Can
                         distinguish between different kinds of tasks,
                         e.g., complex versus simple; Unique versus
                         repetitive.

* Tied

** Tied

[dis] = distracter item

TABLE 1
TIASs Demographic Information
(n = 114)

Panel A: Years of Experience

                           Mean    Median   Std. Dev.      Range

Audit experience           21.57   20.00       6.40     10.00-40.00
Specialty experience       18.40   17.00       7.38      4.00-40.00

Panel B: Industries Represented in Sample

Industry Group               n    Industry Examples

Consumer products/retail    18    Apparel, Food
Financial services          32    Brokerage, Banks
Manufacturing               18    Automotive, High technology
Real estate/construction    10    Home builders, Real estate
Government/public service   15    Higher education, Federal government
Utilities                   10    Electric, Independent power producers
Healthcare                  11    Healthcare systems, Hospitals

Industry Group             Nature of industry

Consumer products/retail   Unregulated
Financial services         Regulated
Manufacturing              Unregulated
Real estate/construction    Unregulated
Government/public service  Regulated
Utilities                  Regulated
Healthcare                 Regulated

TABLE 2
Importance Rankings of TIASs Attributes

Listings by At Least 10 Percent of the TIASs in the Open-Ended
Questionnaire

(1 = minimally important, 2 = mildly important, 3 = moderately
important, 4 = very important, 5 = extremely important)

         (1)                          (2)
      Attribute          Participants' Wording Examples

Knowledge (a)          Knowledge of industry, operations
Communication (a)      Written and oral communication
Technical Skills       Accounting/tax/math/research skills
Experience (a)         Experience
Leadership             Leadership, leader
Recognition            Recognized, well known, visible, reputed
Creativity (a)         Creative, imaginative
People Skills          Friendly, personable, socially skilled
Adaptability (a)       Adaptable, flexible
Intelligent (a)        Intelligent, smart
Commitment             Commits to industry, time
Marketing Skills       Marketing focus, practice building, selling
Consulting             Consulting skills, business advisor, mentor
Problem Solver         Solves problems
Current                Stays current, well read, on cutting edge
Teamwork               Team player, works well with others
Motivation             Motivates self, others, self-starter
Networked              Network, contacts, stays in touch with ...
Confident (a)          Confident, mature
Responsibility (a)     Responsible, visits clients
Training Skills        Trains/develops/educates self and others
Inquisitive (a)        Inquisitive, curious, questions
Active                 Active within and outside firm, visible
Understanding          Understands business, clients and others
Risk Aware             Risk awareness, manages risk
Analytical Skills (a)  Analytical skills, trend recognition
Good Listener (a)      Good listener, good listening skills
Enthusiasm             Enthusiasm, enjoys/loves/passion industry
Public Speaking        Public speaking, makes good presentations
Respected              Respected, eminent
Quick Thinker (a)      Thinks quickly on feet, responsive
Logical (a)            Logical, rational, common sense

         (1)              (3)       (4)       (5)
      Attribute           n        Mean     Median

Knowledge (a)             63       4.81      5.0
Communication (a)         60       4.40      4.5
Technical Skills          59       4.29      4.0
Experience (a)            55       4.42      5.0
Leadership                43       4.33      4.0
Recognition               39       4.31      4.0
Creativity (a)            36       4.33      4.0
People Skills             36       4.14      4.0
Adaptability (a)          36       4.08      4.0
Intelligent (a)           30       4.20      4.0
Commitment                25       4.68      5.0
Marketing Skills          25       4.28      4.0
Consulting                20       4.60      5.0
Problem Solver            20       4.35      4.0
Current                   20       4.25      4.0
Teamwork                  21       4.05      4.0
Motivation                18       4.61      5.0
Networked                 20       4.15      4.0
Confident (a)             19       4.26      4.0
Responsibility (a)        18       4.39      4.0
Training Skills           19       4.00      4.0
Inquisitive (a)           17       3.94      4.0
Active                    16       4.13      4.0
Understanding             14       4.57      5.0
Risk Aware                15       4.00      4.0
Analytical Skills (a)     15       3.93      4.0
Good Listener (a)         13       4.46      5.0
Enthusiasm                13       4.31      5.0
Public Speaking           13       4.23      4.0
Respected                 12       4.50      5.0
Quick Thinker (a)         12       4.33      4.0
Logical (a)               11       4.27      4.0

                         (6)
         (1)             Std.      Weighted (b)
      Attribute          Dev.     Score      Rank

Knowledge (a)            0.43      303         1
Communication (a)        0.67      264         2
Technical Skills         0.67      253         3
Experience (a)           0.83      243         4
Leadership               0.68      186         5
Recognition              0.80      168         6
Creativity (a)           0.68      156         7
People Skills            0.64      149         8
Adaptability (a)         0.81      147         9
Intelligent (a)          0.55      126        10
Commitment               0.56      117        11
Marketing Skills         0.84      107        12
Consulting               0.50       92        13
Problem Solver           0.59       87        14
Current                  0.72       85        15
Teamwork                 0.80       85        16
Motivation               0.50       83        17
Networked                0.67       83        18
Confident (a)            0.56       81        19
Responsibility (a)       0.61       79        20
Training Skills          0.75       76        21
Inquisitive (a)          0.75       67        22
Active                   0.81       66        23
Understanding            0.65       64        24
Risk Aware               0.93       60        25
Analytical Skills (a)    0.70       59        26
Good Listener (a)        0.66       58        27
Enthusiasm               0.85       56        28
Public Speaking          0.83       55        29
Respected                0.52       54        30
Quick Thinker (a)        0.49       52        31
Logical (a)              0.47       47        32

(a) Same as that identified in A&S open-ended attributes in terms of
the 10 percent rule.

(b) The weighted score is based on the number of listings of each
attribute times the importance rating given to the attribute by each
of the TIASs

TABLE 3
Importance Rankings of Pre-Defined Attributes for TIASs
(n = 114)

             (1)                     (2)           (3)
         Attribute               Mean (S.D.)       Rank

Extremely Important
  Current Knowledge (a)        4.47 (0.88) (b)       1
Very Important
  Problem Solver (a)           4.16 (0.96) (b)       2
  Experience (a)               4.14 (0.99)           3
  Perceptive                   4.12 (0.93)           4
  Communicates Expertise (a)   4.10 (1.38)           5
  Self Confidence (a)          4.01 (1.04)           6
  Adaptability (a)             3.81 (1.09)           7
  Intelligence (a)             3.71 (1.05)           8
  Knows What Is Relevant       3.65 (1.02)           9
  Assumes Responsibility (a)   3.63 (1.08)          10
  Quick Thinker (a)            3.45 (1.18) (c)      11
Moderately Important
  Inquisitive (a) [dis]        3.16 (1.06) (c)      12
  Makes Exceptions             2.94 (1.18)          13
  Stress Tolerance             2.87 (1.14)          14
  Configural Processing        2.65 (1.17)          15
  Problem Simplification       2.61 (1.02)          16
  Feedback                     2.54 (1.09)          17
  Assertive [dis]              2.42 (1.15)          18
  Energetic [dis]              2.40 (1.32)          19
  Research Skills (a)          2.38 (1.06) (d)      20
Mildly Important
  Pattern Recognition          1.98 (1.05) (d)      21
  Task Analysis                1.85 (1.02)          22
  Problem Selection            1.70 (0.74) (e)      23
  Minimally Important
  Perfectionist [dis]          1.27 (0.78) (e)      24
  Methodical [dis]             1.11 (0.39)          25

(a) Indicates that this attribute was also identified in the
open-ended results.

(b) These two are significantly different at the .0025 level
(Mann-Whitney W-Statistic = 11,700.5).

(c) These two are significantly different at the .0369 level
(Mann-Whitney W-Statistic = 12,047.5).

(d) These two are significantly different at the .0020 level
(Mann-Whitney W-Statistic = 11,576.5).

(e) These two are significantly different at the .0000 level
(Mann-Whitney W-Statistic = 15,541.5).

[dis] = Distracter item

S.D. = Standard Deviation


The editor, Steve Kaplan, and two anonymous reviewers provided many helpful comments as did Tom Bintinger, Sarah Bonner, Dick Chesley, Martin Evans, John Fogarty, Donald Jones, Jay Thibodeau, Catherine Usoff, Arnie Wright, and the participants at workshops at Bentley College, the University of Utah, and the New England Behavioral Accounting Research Series. Milly Espada, Lynette Groenlay, and Mark Ungewitter provided able research assistance at Bentley College. We gratefully appreciate financial support and top industry audit specialist participation from an anonymous accounting firm in this study.

(1) Specialists have been used as surrogates for experts in prior audit studies. For example, Bedard and Mock (1992) used computer audit specialists (72 months audit experience) as their expert auditors and compared their problem-solving behavior in audit planning to a group of novice auditors (42 months of audit experience). While these studies have measured differences between specialists and nonspecialists, they have not studied top experts in the specialty areas. That is, the "specialty" designation at any level has been used as a surrogate for expertise in the specialty area. Importantly, the sample of senior partners in our study consisted entirely of those designated as TIASs by their firm.

(2) For detailed review papers on this literature, see the special issue of Organizational Behavior and Human Decision Processes 53 (November 1992).

(3) For reviews of the expertise literature in auditing, see Bedard (1989), Choo (1989), Colbert (1989), Davis and Solomon (1989), Bonner and Pennington (1991), Bedard and Chi (1993), and Libby (1995).

(4) The typical dictionary definition of an "expert" is one who possesses a high degree of skill in, or knowledge of, a certain subject. In addition, the popular view is that experts are "people who do a skilled job effortlessly, fluidly, intuitively, and almost never make a mistake" (Trotter 1986, 32) and that "Experts ... do what comes naturally and it almost always works" (Trotter 1986, 36).

(5) Attributes of TIASs is only one potential response to litigation. Other responses might include client screening, modification of fees, and second partner review.

(6) We acknowledge that the selection of attributes in this manner is ad hoc in nature. This is partly due to the fact that there is no generally accepted theory of expert attributes to guide the selection of attributes for inclusion in our study. The ratings form the pre-defined attributes as well as the long list of attributes generated from the open-ended questionnaire in our study provide exploratory evidence.

(7) The high response rate reflects the fact that the national office of the participating accounting firm sanctioned and administered the study within the firm. The study related to the firm's objective of developing practical means of training and evaluating levels of expertise attained by audit specialists at various levels. The authors prepared the task instrument and sent it to the national office, which in turn prepared and sent a packet to each of the 145 TIASs by mail. The TIASs were instructed to return the completed questionnaire to the national office, which collected and sent the responses in bulk to the authors.

(8) We were interested in obtaining the firm's formal files leading to the classification of the target partners. However, due to the proprietary nature of the evaluation process and the confidentiality requirements, we did not obtain access to the process documents and evaluation files.

(9) First, participants chose the five most important attributes, and assigned them to box A, labeled "extremely important." They then selected the next five most important attributes, and assigned them to box B, labeled "very important." They continued this process by completing boxes C then D, and finally put the last five characteristics in box E, labeled "minimally important." The participants also assessed general categories of attributes (e.g., knowledge, cognitive) and also indicated whether the attributes were trainable; these issues are considered outside of the primary scope of the current study.

(10) To code the open-ended response, a research assistant with several years of accounting experience transcribed the listed attributes directly from the TIASs responses. This procedure resulted in a list of 89 attributes.

REFERENCES

Abdolmohammadi, M. L, and J. Shanteau. 1992. Personal attributes of expert auditors. Organizational Behavior and Human Decision Process 53 (November): 158-172.

Alba, J. W., and J. W. Hutchinson. 1987. Dimensions of consumer expertise. Journal of Consumer Research 13 (March): 411-454.

Anderson, J. R. 1981. Cognitive Skills and Their Cognition. Hillsdale, NJ: Erlbaum.

Ballou, B. 2001. The relationship between auditor characteristics and the nature of review notes for analytical procedure working papers. Behavioral Research in Accounting 13: 25-48.

Bedard, J. 1989. Expertise in auditing: Myth or reality. Accounting, Organizations and Society 14 (l&2): 113-131.

--, and M. T. H. Chi. 1993. Expertise in auditing. Auditing: A Journal of Practice & Theory 12 (Supplement): 21-45.

Bedard, J. C., and S. F. Biggs. 1991. Pattern recognition, hypothesis generation, and auditor performance in analytical tasks. The Accounting Review 66 (July): 622-642.

Bedard, J., and T. J. Mock. 1992. Expert and novice problem-solving behavior in audit planning. Auditing: A Journal of Practice & Theory 11 (Supplement): 1-20.

Bhamornsiri, S., and R. E. Guinn. 1991. The road to partnership in the "Big Six" firms: Implications for accounting education. Issues in Accounting Education 6 (Spring): 9-24.

Bonner, S., and B, Lewis. 1990. Determinants of auditor expertise. Journal of Accounting Research 28 (Supplement): 1-20.

--, and N. Pennington. 1991. Cognitive processes and knowledge as determinants of auditor expertise. Journal of Accounting Literature 10: 1-50.

Brown, C. E., and I. Solomon. 1991. Configural information processing in auditing: The role of domain-specific knowledge. The Accounting Review (January): 100-119.

Chi, M. T. H., R. Glaser, and M. J. Fan'. 1988. The Nature of Expertise. Hillsdale, NJ: Erlbaum.

--. 1991. Modeling expertise. In In the Mind's Eye: Enhancing Human Performance, edited by D. Druckman, and R. A. Bjork, 57-79. Washington D.C.: National Academy Press.

Choo, F. 1989. Expert-Novice differences in judgment/decision making research. Journal of Accounting Literature 8: 106-136.

Colbert, J. L. 1989. The effect of experience on auditors' judgments. Journal of Accounting Literature 8: 137-149.

Davis, J. S., and I. Solomon. 1989. Experience, expertise, and expert-performance research in accounting. Journal of Accounting Literature 8: 150-164.

Emby, C., and L. D. Etherington. 1996. Performance evaluation of auditors: Role of perceptions of superiors and subordinates. Auditing: A Journal of Practice & Theory 15 (Fall): 99-109.

Emerson, J. 1993. KPMG Peat Marwick: Setting the new practice classification scheme standard. Professional Services Review 82: 1.

Graham, L. E. 1993. Discussion of: Expertise in auditing. Auditing: A Journal of Practice & Theory 12 (Supplement): 46-50.

Hirst, M. K., and P. F. Luckett. 1992. The relative effectiveness of different types of feedback in performance evaluations. Behavioral Research in Accounting 4: 1-21.

Libby, R., and J. Luft. 1993. Determinants of judgment performance in accounting settings: Ability, knowledge, motivation, and environment. Accounting, Organizations and Society 18 (5): 425-450.

--, and H. T. Tan. 1994. Modeling determinants of audit expertise. Accounting, Organizations and Society (November): 701-716.

--. 1995. The role of knowledge and memory in audit judgment. In Judgment and Decision Making Research in Accounting and Auditing, edited by A. H. Ashton, and R. H. Ashton, 176-206. New York, NY: Cambridge University Press.

Maletta, M., and A. Wright. 1996. Audit evidence planning: An examination of industry error characteristics. Auditing: A Journal of Practice & Theory 15 (Spring): 71-86.

Pasewark, W. R., J. R. Strawser, and J. E. Wilkerson, Jr. 1993. An empirical examination of the relationships among leader behaviors, audit team performance, and staff satisfaction. Paper presented at the American Accounting Association Annual Meeting, San Francisco.

Salterio, S. 1994. Researching for accounting precedents: Learning, efficiency, and effectiveness. Contemporary Accounting Research 11 (Fall): 515-542.

Shanteau, J., and T. R. Stewart. 1992. Why study expert decision making? Some historical perspectives and comments. Organizational Behavior and Human Decision Process 53 (November): 95-106.

Shpilberg, D., L. Graham, and H. Schatz. 1986. ExperTAX: An expert system for corporate tax planning. Reprint from Expert Systems (July): 136-150.

Solomon, I., M.D. Shields, and O. R. Whittington. 1999. What do industry-specialist auditors know? Journal of Accounting Research 37 (Spring): 191-208.

Tan, H. T., and R. Libby. 1997. Tacit managerial versus technical knowledge as determinants of audit expertise in the field. Journal of Accounting Research 35 (Spring): 97-113.

--. 1999. Organizational levels and perceived importance of attributes for superior audit performance. Abacus 35 (1): 77-90.

Trotter, R. J. 1986. The mystery of mastery. Psychology Today (July): 32-38.

U.S. Congress. 2002. The Sarbanes-Oxley Act of 2002. 107th Congress of the United States of America. H.R. 3763. Washington, D.C.: Government Printing Office.

Wright, S., and A. Wright. 1997. The effect of industry experience on hypothesis generation and audit planning decisions. Behavioral Research in Accounting 9: 273-294.

Wright, W. F. 2001. Task experience as predictor of superior loan loss judgments. Auditing: A Journal of Practice & Theory 20 (1): 147-155.
COPYRIGHT 2004 American Accounting Association
No portion of this article can be reproduced without the express written permission from the copyright holder.
Copyright 2004 Gale, Cengage Learning. All rights reserved.

Article Details
Printer friendly Cite/link Email Feedback
Author:Abdolmohammadi, Mohammad J.; Searfoss, D. Gerald; Shanteau, James
Publication:Behavioral Research in Accounting
Date:Jan 1, 2004
Words:9200
Previous Article:A note on self-explanation as a training tool for novice auditors: the effects of outcome feedback timing and level of reasoning on performance.
Next Article:The role of cognitive and affective conflict in early implementation of activity-based cost management.
Topics:

Terms of use | Privacy policy | Copyright © 2019 Farlex, Inc. | Feedback | For webmasters