chief audit executives' assessment of internal auditors' performance attributes by...

BEHAVIORAL RESEARCH IN ACCOUNTING American Accounting AssociationVol. 24, No. 1 DOI: 10.2308/bria-500232012pp. 1–23

Chief Audit Executives’ Assessment ofInternal Auditors’ Performance Attributes by

Professional Rank and Cultural Cluster

Mohammad J. Abdolmohammadi

Bentley University

ABSTRACT: This study explores chief audit executives’ perceptions of the most

important performance attributes of internal auditors by professional rank and cultural

cluster. A large sample of chief audit executives (CAEs) from 19 countries located in five

different cultural clusters was surveyed by the Institute of Internal Auditors Research

Foundation (CBOK 2006). Analysis of data generated by that survey indicates that while

leadership attributes (e.g., negotiating) are perceived to be most important for upper

ranks in internal auditing, technical skills (e.g., analytical) are most important for lower

ranks. Also, based on the cultural relativism literature, I hypothesize and find evidence

that the importance of performance attributes differs significantly by cultural cluster. For

example, while Latin-American CAEs rated leadership attributes at higher levels than

other cultural clusters for internal audit staff, the East-European chief audit executives

assessed the importance of technical skills at higher levels than other clusters. The

results of the survey provide important initial empirical support to the list of ‘‘ideal’’ and

‘‘desirable’’ performance attributes for internal auditors recently developed by the

Institute of Internal Auditors (IIA 2009).

Keywords: internal auditor attributes; professional rank; culture.

Data Availability: The source of data used in this study is CBOK (2006) from the

Institute of Internal Auditors Research Foundation.

INTRODUCTION

This study explores chief audit executives’ (CAE) perceptions of the most important

performance attributes of internal auditors by professional rank and cultural cluster. To

date, few other studies have investigated this issue, and those that do focus on objectivity

This paper has benefited greatly from helpful comments by two anonymous reviewers and the editor, Theresa Libby.Also, comments by Audrey Gramling, Allen Hartt, Alan Reinstein, Gerrit Sarens, participants at the 2010 annualmeetings of the American Accounting Association (particularly the discussant, Gary Braun), the Midwest Regionalmeeting of the American Accounting Association (particularly the discussant, Nathan Stewart), and the EuropeanAccounting Association were helpful in revising the paper. The source of data for the study is the Common Body ofKnowledge (CBOK) in Internal Auditing database. The Institute of Internal Auditors Research Foundation (IIARF)developed CBOK in 2006–2007, and the author was a member of the research team that helped with its development.The author is grateful to the numerous anonymous internal auditing professionals who participated in the study, and theIIARF for its permission to use the database.

Published Online: December 2011

1

alone (e.g., Brody and Lowe 2000; Schneider 2003; Ahlawat and Lowe 2004). A reason for this

dearth of information may have been lack of data in the past. The development of the CBOK (2006)

database by the Institute of Internal Auditors Research Foundation (IIARF) in recent years provides

a unique opportunity to identify important performance attributes for internal auditing. This

database is used in the current study.

This study has three objectives. The first is to document the perceived most important

performance attributes for the practice of internal auditing. An early study in external auditing

argues that knowledge of detailed performance attributes can assist in, among other things,

designing decision aids, developing training programs, specifying guidelines for hiring, and

establishing procedures for employee evaluation/promotion (Abdolmohammadi and Shanteau

1992). A similar argument can be made for the importance of documenting detailed performance

attributes for the practice of internal auditing. Knowledge of these performance attributes can also

be useful for behavioral research. For example, the results of the study can help behavioral

researchers by providing them with various performance attributes, such as leadership attributes,

that may be needed to perform at the CAE level. Behavioral studies that focus on designing training

programs to enhance various performance attributes can also benefit from consulting the list of

perceived important attributes for various professional ranks of internal auditors.

This objective is particularly important in the post Sarbanes-Oxley Act (SOX, U.S. House of

Representatives 2002) era, when understanding of performance attributes can help companies to

improve systematically the quality of their internal audit practice. Before the SOX (2002)

legislation, internal audit functions engaged in extensive outsourcing of certain internal audit

activities, such as EDP audit expertise (Abbott et al. 2007). This is because outsourcing was

perceived to be an efficient way to improve the quality of both internal and external audits (IIA

1994). However, to improve external auditor independence, SOX (2002) Section 201 prohibited

outsourcing of internal audit activities to incumbent external auditors. Concurrently, SOX (2002)

Section 404 increased management responsibility for effective internal controls, thus increasing the

role of the internal audit function to assess internal control effectiveness. While internal audit

functions are not prohibited from outsourcing their internal audit activities to parties other than their

external auditors in the post-SOX (2002) era, many companies have chosen to improve their own

internal audit capabilities to reduce the extent of outsourcing. The CBOK (2006) database indicates

that by 2006, the majority of the worldwide companies in the database, 68.8 percent, outsourced

only 10 percent or less of their internal audit activities.

The second objective of the current study is to provide evidence of differences in important

performance attributes by various professional ranks. In external auditing, the literature suggests

that, generally, technical skills are more important for lower professional ranks, while leadership

attributes are more important for higher professional ranks (Bhamornsiri and Guinn 1991; Emby

and Etherington 1996; Tan and Libby 1997). Understanding these differences in internal auditing

can assist behavioral research targeted to staff training and job assignments in internal auditing.

Specifically, while technical skills can be targeted for training of lower ranks, leadership attributes

can be targeted for training at the internal audit manager and CAE levels. As Solomon and Shields

(1995) have observed, one of the objectives of behavioral research in auditing is training

enhancement.

Like external auditing, performance attribute differences by rank can also be expected in

internal auditing, but the nature of the variation may be different. This is due to some similarities

(e.g., similar audit techniques) but also major differences in practice between internal and external

auditing. Richards (2005) summarizes differences in areas of standards (IIA versus PCAOB

standards), audit approaches (e.g., customized versus risk based), independence (within

organization objectivity versus organizational independence for external auditors), audit results

(recommendation/facilitation versus audit opinion), internal control assessment (full internal control

2 Abdolmohammadi

Behavioral Research In AccountingVolume 24, Number 1, 2012

assessment versus opinion on management’s assessment of internal controls), fraud risk assessment

(investigation of all kinds of fraud versus financial fraud), and follow-up (continuous to correct

weaknesses versus follow-up as a part of the next audit). These differences raise a question as to

whether or not the results of studies in external auditing generalize to internal auditing. For

example, while external auditors must have detailed knowledge of the PCAOB standards for their

financial audits, internal auditors must have detailed knowledge of the IIA standards as well as the

PCAOB standards (especially as they relate to assessment of the system of internal controls) to

perform their internal audits.

The final objective of this study is to investigate differences in performance attributes by

cultural clusters. Prior studies of performance attributes in external auditing, and a few studies of

objectivity in internal auditing, have been conducted in a single country context. The availability of

data from various countries in the CBOK (2006) database makes it possible to extend the literature

to multiple countries in various cultural clusters.

Hofstede’s (1980; 2001) cultural relativism theory suggests that professional judgment is

influenced by one’s cultural background. Since the practice of internal auditing requires various

professional judgments, the importance of performance attributes is expected to be influenced by

culture. Following Gray (1988), a number of studies have documented the effects of culture on the

development of accounting as a profession and have reported differences between cultures. For

example, Doupnik and Salter (1995) tested the effects of culture on the development of

international accounting and found culture to have an effect. More recently, Doupnik (2008)

demonstrated cross-national differences in earnings management due to cultural differences, and

Tsakumis (2007) found that Greek accountants are less likely to disclose information than U.S.

accountants. In the current study, guidance is used from a study of 62 societies (called the GLOBEStudy) by House et al. (2004) to classify 19 countries into five distinct cultural clusters of

Anglo-Saxon, East-European, Germanic-Europe, Latin-American, and Latin-European. The effects

of this classification on various performance attributes are then investigated.

A large sample of CAEs in the current study assessed 18 of a list of 43 attributes as the most

important performance attributes for the practice of internal auditing. Through factor analysis, these

attributes were classified into leadership attributes (e.g., negotiating) and technical skills (e.g.,

analytical). The results indicate that while leadership attributes are more important for upper ranks,

technical skills are more important for lower ranks. Further analysis reveals significant differences

by cultural cluster. For example, while Latin-American CAEs rated leadership attributes at higher

levels than other cultural clusters for staff, the East-European CAEs assessed the importance of

technical skills at higher levels than other clusters for CAEs. More details of these results are

provided in the rest of the paper.

The rest of the paper reviews the background literature and develops the study’s research

questions and hypothesis. This is followed by the Method section, which describes the source of the

data. Analyses of the data are provided in the Results section, followed by a summary and a number

of conclusions from the study in the final section.

BACKGROUND LITERATURE, RESEARCH QUESTIONS, AND HYPOTHESISPerformance Attributes

Performance attributes are a collection of behavioral skills (e.g., leadership), technical skills

(e.g., data collection and analysis), and competencies (e.g., communication skills) that are needed

for auditors to perform their tasks effectively and efficiently. Early research (e.g., Bonner and Lewis

1990; Libby 1995; Libby and Luft 1993; and Libby and Tan 1994) provides evidence on attributes

such as ability, knowledge, and experience as determinants of superior performance in external

auditing.

Chief Audit Executives’ Assessment of Internal Auditors’ Performance Attributes 3


External auditing research also suggests that as auditors move higher in their professional

ranks, they achieve improvements in efficiency and effectiveness in their performance (Salterio

1994). Wright (2001) adds that auditors’ judgment performance improves by achieving higher

levels of professional rank. Abdolmohammadi (2008) reports that, except for innate attributes (e.g.,

intelligence), the possession of performance attributes generally increases with professional rank in

external auditing. Other studies in external auditing (e.g., Abdolmohammadi and Shanteau 1992;

Libby and Tan 1994; Tan 1999; Abdolmohammadi et al. 2004) have proposed performance

attributes that were not widely considered previously, including leadership attributes such as

management of self, others, and career (called tacit managerial knowledge), which Tan and Libby

(1997) found to be important to external auditing. Finally, from a study of industry audit specialists,

Abdolmohammadi et al. (2004) identified a detailed list of performance attributes for external

auditors. Their finding indicates the importance of knowledge, leadership, technical skills, and

communication skills for the practice of external auditing.

Most Important Performance Attributes of Internal Auditors

While there have been numerous studies of performance attributes in external auditing, only a

few studies have addressed performance attributes in internal auditing, and the focus of these

studies has been limited to objectivity. For example, Brody and Lowe (2000) discuss the

implications of the evolving roles of internal auditors for their objectivity. Schneider (2003)

presents empirical data on whether incentive compensation and stock ownership affect internal

auditors’ objectivity, and Ahlawat and Lowe (2004) compare objectivity in internal auditing

between functions that are retained in-house versus those that are outsourced. Other differences in

practice between external and internal auditing may result in differences in importance of attributes

between the two types of auditing. For example, while the basic audit skill set is the same for

internal and external auditing, these auditing contexts are subject to the use of a different mix of

standards (i.e., PCAOB standards for external auditing as compared to both PCAOB and IIA

standards for internal auditing).

The Internal Auditor Competency Framework (IIA 2009) is a list of performance attributes for

internal auditors that the Institute of Internal Auditors calls ‘‘ideal’’ and ‘‘desirable.’’ The framework

is presented as ‘‘the minimum level of knowledge and skills needed to effectively operate and

maintain an internal audit function’’ (IIA 2009). Compiled by ‘‘subject matter experts and

volunteers, the competencies and rankings [in this framework] were developed by a global task

force’’ (IIA 2009); however, there is no indication that the task force collected or analyzed

systematic data to support the framework.

The IIA framework organizes performance attributes into four classes: interpersonal skills,

tools and techniques, internal audit standards, and knowledge areas. These general categories are

further divided into subcategories and then into very detailed attributes. For example, interpersonal

skills contain subcategories of communication, management, leadership, conflict management,

team capabilities, collaboration, and cooperation skills. These subcategories then list more detailed

competencies. For example, the subcategory of communication skills contains a number of detailed

competencies such as ‘‘Can communicate clearly with senior executives and board-level

individuals’’ (IIA 2009). The current study addresses the framework’s subcategory level through

the following research question.

RQ1: What are the most important performance attributes of internal auditors?

Important Attribute Differences by Professional Rank

The literature reviewed earlier (e.g., Salterio 1994; Tan and Libby 1997; Wright 2001;

Abdolmohammadi 2008) suggests that while the importance of leadership attributes increases with

4 Abdolmohammadi


professional rank, the importance of technical skills decreases by professional rank.1 As discussed

earlier, while there are similarities, there are also major differences between internal auditing and

external auditing. The differences are likely to result in differences in the importance of

performance attributes. However, the direction of these differences is unclear. Thus, I present an

exploratory research question as follows:

RQ2: Does the importance of performance attributes differ by internal auditors’ professional

rank?

Effects of Culture on Performance Attributes

The theory of cultural relativism advanced by Hofstede (1980) and refined by Hofstede (2001)

and House et al. (2004) posits that cultural differences have significant effects on the development

and operations of various professional practices that rely on professional judgment. Hofstede (1980)

proposed the theory, which he used later to classify 50 countries into various cultural clusters

(Hofstede 1983). House et al. (2004) extended Hofstede’s work by classifying 62 societies into

various cultural clusters. As detailed in the Method section, House et al.’s (2004) results are used to

identify the countries in the current study into five distinct cultural clusters.

Based on Hofstede’s (1980) work, Gray (1988) argued for the relevance of cultural relativism

to the development of the accounting profession in various countries. Gray’s work has motivated a

number of studies that have found the development of international accounting to be affected by

culture (Doupnik and Salter 1995) and have explained differences in financial reporting practices in

29 countries (Salter and Niswander 1995). Tsakumis (2007) reports that national culture plays a

role in accountants’ disclosure judgments, and investigating cross-national differences in earnings

management, Doupnik (2008) and Braun and Rodriguez (2008) report significant effects for

cultural differences on earnings management in various countries.

Extending this literature to internal auditing, Abdolmohammadi and Tucker (2002) show that

the development of internal auditing as a profession is associated with a number of variables,

including culture. However, very little literature has developed on the effects of culture on the

practice of internal auditing. Thus, the literature on the development of the accounting profession as

summarized above is used as a guide to extend this literature to internal auditing, where IIA

affiliates (now called institutes) are located in over 100 countries (IIA 2008). In the current study,

cultural clusters are used to investigate the effects of culture on perceived importance ratings of

performance attributes of internal auditors. Because setting up a specific hypothesis for each

attribute by culture will result in a long list of hypotheses, only one general hypothesis is stated here

to be tested at the factor level (i.e., leadership attributes and technical skills) and at the individual

attribute level by professional rank.

H1: Importance of performance attributes of internal auditors will differ by cultural cluster.

RESEARCH METHOD

The source of data for this study is the IIA’s CBOK (2006) database. This database contains

responses from internal auditors of varying ranks practicing in over 100 countries. The IIARF

developed this database in 2006 as a comprehensive study of the current state of the internal

1 The decreasing level of importance does not mean lack of possession of these attributes. As Abdolmohammadi(2008) finds in external auditing, the possession of these attributes may stay the same as one moves up theprofessional ranks. However, the importance of these attributes to perform at the high professional ranks will belower than leadership attributes.



auditing profession worldwide. The data collected range from personal attributes of internal

auditors (e.g., education), to the characteristics of their organizations (e.g., number of employees),

to the internal and external quality assessment of the internal audit function. Included are data on 43

performance attributes of internal auditors.

While CBOK (2006) has no data on cultural cluster classification, House et al. (2004) identify

various cultural clusters for 62 societies. Matching data from CBOK (2006) and House et al.

(2004), the current study identified 19 countries that could be classified into five distinct cultural

clusters for investigation. Specifically, two criteria were used to select countries from CBOK (2006)

for the current study. First, the country to be selected had to be clearly identified with a specific

cultural cluster by House et al. (2004). House et al. (2004) have cultural classifications for 62

societies, where a given country can have two or more cultural clusters. For example, Canada has

both Anglo-Saxon and French societies, and Switzerland has both French and Germanic societies.

Because CBOK (2006) does not distinguish cultural clusters within countries, the multi-society

countries were not included in this study.

Second, to be included a cultural cluster had to be represented by at least ten observations in

the CBOK (2006) database so as to have sufficient data for statistical analysis. The resulting sample

used in this study consists of 1,497 responses from CAEs in 19 countries classified into five distinct

cultural clusters. Table 1 presents a summary of the sample groups by cultural cluster and country.

The Anglo-Saxon cluster has the largest number of CAE responses with 913 observations, while the

East-European cluster has only 58 responses. Within various clusters, Venezuela, with seven

responses, has the smallest sample size, and the U.S., with 760 responses, has the largest sample

size.

DATA ANALYSIS

Demographic Data

Table 2 contains demographic information, where analysis of variance (ANOVA) is used to

test differences by cultural cluster for continuous variables (e.g., CAE age), and the v2 test is used

for tests of differences in discrete variables (e.g., graduate/undergraduate education). The results

indicate that, overall, the subjects had an average age of 45.37 years with a low of 37.37 years for

Eastern Europe, and a high of 46.95 for Germanic Europe. The differences are highly significant,

with F¼ 17.21, p , 0.001. The CAEs from Eastern Europe also had the lowest years of experience

as CAE (4.36 years) and Germanic Europe had the highest number of years (8.73 years), where the

ANOVA indicated highly significant differences (F ¼ 10.27, p , 0.001). As shown in the last

column of Table 2, other demographic variables also indicate significant differences by cultural

cluster. This includes both continuous and discrete variables. For example, Table 2 shows that,

overall, 54 percent (46 percent) of the CAEs had been members of the IIA for six years or more

(five years or less) with Anglo-culture countries having the largest percentage (63.4 percent) and

Eastern Europe having the lowest (20.7 percent), where the differences are highly significant (v2¼119.27, p , 0.001).

Table 2 also presents the size of the internal audit function of the participating CAEs. A size of

0–3 professionals indicates that some internal audit functions may have had only a part-time

position, or a combination of part-time and full-time positions. Overall, 39.7 percent of the

participants were from internal audit functions with 3 or fewer internal auditors, 39.8 percent were

from internal audit functions of 4–12 professionals, 9.5 percent were from functions with 13–24

professionals, leaving 11.0 percent of participants from large audit function of 25 or more

professionals. The differences by cultural cluster are highly significant (v2¼ 35.78, p , 0.001). The

final item in Table 2 presents data on the size of the CAEs’ organizations in terms of the number of

6 Abdolmohammadi


full-time equivalent employees. As expected, there is much variation in size between various

cultural clusters.

Analysis of the Importance of Attributes by Professional Rank (RQ1 and RQ2)

CAEs were provided with a list of 43 attributes to select the most important ones in each of the

three categories of behavioral skills, technical skills, and competencies for each of four professional

ranks (staff, senior/supervisor, manager, and CAE).

Behavioral SkillsBehavioral skills cover a wide range of attributes such as facilitating, communication skills,

and leadership. Twelve behavioral attributes were listed in CBOK from which CAEs picked the five

most important attributes for the practice of internal auditing at various professional ranks. Table 3,

Panel A presents the results. The behavioral attributes that were selected by at least 50 percent of

CAEs for at least one of the professional ranks are bolded in Table 3. This analysis identifies eight

attributes (confidentiality, governance and ethics sensitivity, interpersonal skills, leadership,

TABLE 1

Cultural Clusters and Countries in the Sample

Cultural Cluster Countries Sample Size

Anglo-Saxon Australia 72

New Zealand 13

UK/Ireland 68

United States 760

Cluster Total 913

Eastern Europe Poland 8

Russia 50

Cluster Total 58

Germanic Europe Austria 57

Germany 40

The Netherlands 26

Cluster Total 123

Latin America Argentina 13

Brazil 11

Colombia 25

Costa Rica 16

Mexico 24

Venezuela 7

Cluster Total 96

Latin Europe France 69

Italy 129

Portugal 14

Spain 95

Cluster Total 307

Grand Total 1,497



objectivity, team player, working independently, and working well with all levels of management)

as the most important behavioral attributes.

As reported in the last column of Table 3, the Cochran test shows differences by professional

rank for all behavioral attributes in the table (not just those perceived by a majority of CAEs as most

important) at high levels of significance.2 For example, leadership was identified by 78 percent of

CAEs as a most important attribute for CAEs. In comparison, most important ratings of leadership

provided by CAEs were 47 percent for internal audit managers (IAMs), 20 percent for seniors/

supervisors, and only 3 percent for staff. These differences by rank were highly significant

(Cochran’s Q¼ 1,872.34, p , 0.001). The attributes of team player and working independently are

viewed as more important for lower professional ranks than upper ranks. As bolded in Table 3, of

TABLE 2

Sample Characteristics by Cultural Cluster (Mean [Std. Dev.] for Continuous Variablesand Percentages for Discrete Variables)

Demographic Variable

Cultural Cluster

TotalAnglo-Saxon

EastEuropean

GermanicEurope

LatinAmerican

LatinEuropean

Statistic(Sig.)

CAE Age (years) 46.21 37.37 46.95 43.21 44.49 45.37 F ¼ 17.21

(8.44) (6.74) (8.14) (7.56) (8.08) (8.45) (, 0.001)

Years as CAE 6.94 4.36 8.73 7.50 5.28 6.69 F ¼ 10.27

(6.46) (2.69) (7.07) (5.98) (4.87) (6.16) (, 0.001)

Years of Internal Audit

Experience

14.04 5.60 11.94 13.51 7.95 12.26 F ¼ 33.97

(9.80) (3.52) (7.90) (10.63) (5.70) (9.26) (, 0.001)

Years of IIA Membership:

5 or less 36.6% 79.3% 38.5% 69.1% 63.5% 46.0% v2 ¼ 119.27

6 or more 63.4% 20.7% 61.5% 30.9% 36.5% 54.0% (, 0.001)

% Having Graduate Ed. 38.6% 27.6% 70.7% 42.7% 39.1% 41.2% v2 ¼51.99

(, 0.001)

% Having AC/AU/IA

major

74.4% 43.1% 55.3% 88.5% 38.8% 65.2% v2 ¼ 169.27

(, 0.001)

% Having CIA Cert. or

Equivalent

48.0% 13.8% 39.0% 30.2% 24.4% 39.9% v2 ¼ 75.71

(, 0.001)

Size (fulltime IA professionals):

00–03 43.0% 35.7% 47.0% 22.3% 33.3% 39.7%

04–12 38.9% 42.9% 35.7% 46.8% 41.2% 39.8%

13–24 8.9% 16.1% 6.1% 14.9% 9.9% 9.5% v2 ¼ 35.78

25 or more 9.2% 5.4% 11.3% 16.0% 15.6% 11.0% (, 0.001)

Size (full-time employees):

500 or less 27.7% 41.1% 30.8% 48.4% 22.1% 28.6%

501–5000 44.9% 35.7% 42.5% 37.9% 44.1% 43.7% v2 ¼ 34.13

5001 or above 27.4% 23.2% 26.7% 13.7% 33.8% 27.6% (, 0.001)

2 The Cochran test is a substitute for the Chi-square test in situations where the responses are not independent.Since CAEs concurrently made judgments about the importance of attributes for the four professional ranks, theCochran test of related samples was used to investigate differences in importance of each attribute byprofessional rank.

8 Abdolmohammadi


the 12 attributes listed, 8 were selected by at least 50 percent of CAEs as important for at least one

of the professional ranks.

Technical SkillsPanel B in Table 3 shows CAEs’ assessment of 13 technical skills, 6 of which were selected by

at least 50 percent of CAEs for at least one of the professional ranks (bolded in the table). They are

data collection and analysis, identifying types of controls, interviewing, negotiating, risk analysis,

and understanding business. The statistical analysis shows significant differences by professional

rank for all but one attribute, financial analysis, where the Cochran Q is only 2.52 and not

statistically significant at conventional levels. The ratings for this attribute are all in the 30 percent

range, thus it was not selected as one of the most important attributes. Also, for some skills, the

importance increases by professional rank (e.g., risk analysis, negotiating, and understanding

business), while for other skills (mostly technical skills), it decreases by professional rank (e.g., data

collection and analysis, interviewing, identifying controls).

CompetenciesCBOK listed 18 competencies from which CAEs identified the 5 as the most important for each

professional rank. As bolded in Table 3, Panel C, five competencies—ability to promote the internal

audit in organization, analytical ability to work through an issue, communication, and writing

skills—were identified (bolded in the table) by at least 50 percent of CAEs as important for at least

one of the professional ranks. The Cochran’s Q statistic shows highly significant differences by

professional rank for all 18 competencies at the 0.004 level. Also, the results show that while

importance increases by professional rank for ability to promote internal auditing in the

organization, for analytical ability and writing skills, the importance decreases by professional rank.An anomaly in Table 3 is that while communication is indicated as more important at the staff and

CAE levels, it is less important for senior/supervisors and internal audit managers.

Overall SummaryA total of 18 attributes were listed by at least 50 percent of CAEs as the most important

attributes for at least one of the four professional ranks. These attributes provide evidence to address

RQ1. Table 3 also shows that for 42 of the original 43 attributes, CAE ratings were significantly

different by professional rank, providing evidence on difference in perceived importance of

performance attributes by professional rank (RQ2).

Principal Components Factor Analysis (PCA) was performed to classify the 18 most important

attributes into factors. For example, setting the number of desired factors at two, CAE perceptions

of the most important attributes for staff were subjected to PCA analysis. Table 4 lists the resulting

two factors that collectively explain 28.91 percent of the total variation in responses. Specifically,

Factor 1 accounts for 20.21 percent of variation, and Factor 2 accounts for 8.70 percent of the

variation.3 The loading weights of the 18 attributes for each of the two factors are also presented in

Table 4. Using the largest factor loadings of Factors 1 and 2 results in identification of 13 attributes

in Factor 1 and 5 attributes in Factor 2.

The 13 attributes in Factor 1 (confidentiality, interpersonal skills, objectivity, team player,

working independently, work with all levels of management, data collection and analysis, identifying

types of controls, interviewing, understanding business, analytical, communication, and writing skills)

3 Expanding the number of factors to more than 2 may be viewed as desirable, where up to 18 factors can bedriven. However, as evidenced by only 8.70 percent of variance explained by Factor 2 (as compared with 20.21percent for Factor 1) in Table 4, each of the remaining 16 factors will explain a small single digit share of thetotal variance explained.



TABLE 3

Percentage of CAEs Selecting Each Attribute as Most Important for Each Professional Rank(Bold Data Are Those that Indicate at Least 50 Percent CAE Selection for at Least One of the

Four Professional Ranks)

Panel A: Behavioral Skills

Performance Attribute

% of CAEs Selecting the Attributeas the Most Important

Cochran QaStaff Sr/Supr IAM CAE

a. Confidentiality 73 56 52 69 316.73b. Facilitating 11 26 32 35 270.39c. Governance and ethics sensitivity 22 20 28 52 627.04d. Interpersonal skills 69 50 45 50 269.25e. Leadership 3 20 47 78 1,872.34f. Objectivity 75 54 43 56 379.35g. Relationship building 28 24 32 47 259.73h. Staff management 2 30 44 36 690.83i. Team building 6 30 39 33 477.26j. Team player 67 35 17 13 1,302.40k. Working independently 57 26 15 16 852.86l. Work well with all levels

of management

42 35 43 59 208.40

Panel B: Technical Skills


Cochran QaStaff Sr/Supr IAM CAE

a. Data collection and analysis 78 34 14 13 1,607.80b. Financial analysis 34 35 34 36 2.52

c. Forensic skills/fraud awareness 20 31 39 41 209.38d. Identify types of controls

(e.g., preventive, detective)

55 44 33 30 248.71

e. Interviewing 60 45 36 36 240.67f. ISO/quality knowledge 5 7 13 16 164.46g. Negotiating 7 24 50 73 1,530.79h. Research skills 45 31 19 20 337.46i. Risk analysis 26 40 59 78 956.28j. Statistical sampling 28 20 8 5 435.98k. Total quality management 2 8 17 30 597.99l. Understanding business 45 43 60 83 758.97m. Use of information technology 49 37 29 27 254.53

(continued on next page)

10 Abdolmohammadi


encompass various skills for internal auditors, and thus can broadly be defined as technical skills. On

the other hand, the five attributes in Factor 2 (governance and ethics sensitivity, leadership,

negotiating, risk analysis, and ability to promote the internal audit function) represent leadership

abilities, and thus can be defined as leadership attributes. These factor classification names are

consistent with that used in prior studies of external auditing. PCA analyses were also performed on

the 18 attributes for senior/supervisors, IAMs, and CAEs. With some minor variations (e.g.,

‘‘communication’’ was not a clear choice in some of the PCAs, indicating that communication is

important to all professional ranks), the results were generally consistent with those for staff.

Using the two factors identified, I calculated an aggregate importance rating scale, where CAE

choices of the 13 technical skills and the 5 leadership attributes were totaled and then divided by the

number of attributes in each factor (13 and 5, respectively) to calculate the means, standard

deviations, and ranks of importance by professional rank. The results are presented in Table 5,

where the Kendall test of k-related samples is used to analyze differences by rank. As reported in

Table 5 and depicted in Figure 1, the importance of leadership attributes increases with professional

rank (Kendall-W¼ 0.659, p , 0.001). These results provide evidence to address RQ2. Table 5 and

Figure 1 also show that for the technical skills factor, the importance ratings decrease by

professional rank up to the rank of IAM, and then slightly reverse (i.e., become more important) for

CAEs. The statistically significant results suggest that technical skills are more important for the

lower professional ranks than for upper ranks (Kendall-W ¼ 0.333, p , 0.001). These results

provide additional evidence to address RQ2.

TABLE 3 (continued)

Panel C: Competencies


Cochran QbStaff Sr/Supr IAM CAE

a. Ability to promote IA in organization 8 9 27 79 2,140.61b. Analytical (ability to work through an issue) 66 47 29 24 660.23c. Change management 4 6 17 34 670.31d. Communication 50 42 46 62 181.60e. Conflict resolution 6 19 33 41 573.30f. Critical thinking 47 32 25 31 202.28g. Conceptual thinking 18 16 20 38 282.85h. Foreign language skills 11 7 9 11 30.84i. Keeping up with standards and regulations 19 21 30 41 255.59j. Organization skills 25 22 26 29 17.17k. Presentation skills 10 14 23 37 414.86l. Problem identification and solution 46 33 22 21 310.82m. Project planning and management 7 24 36 25 349.07n. Report development 20 28 24 12 125.40o. Time management 42 27 17 14 438.73p. Training and developing staff 2 19 39 30 611.68q. Understanding complex information systems 14 14 14 11 13.23r. Writing skills 52 34 25 24 397.83

a Bold Cochran Qs are significant at the 0.001 level.b Bold Cochran Qs are significant at the 0.004 level or lower.



Effects of Culture on Performance Attributes (H1)

To test for the relationship between cultural clusters and performance attributes, the leadership

attributes and technical skills factors identified in the previous section were analyzed by cultural

clusters, using the analysis of variance (ANOVA) for each of the four professional ranks. The

results are reported in Table 6, where factor means (i.e., mean proportions of CAEs selecting the

attributes in each factor) are presented along with their standard deviations and rank orders, and

those that are significantly different from the overall means are bolded. Viewed vertically, Table 6

shows that for each cultural cluster the importance of leadership attributes increases by professional

rank, while the importance of technical skills decreases by professional rank up to the IAMs, where

it reverses and becomes more important for CAEs. This finding is consistent with the analysis

reported earlier in Table 5 and Figure 1.

Viewed horizontally, the data in Table 6 show highly significant differences between cultural

clusters for both leadership attributes and technical skills. Specifically, the ANOVA analysis of

leadership attributes for staff has an F-statistic of 25.20, which is highly significant at the 0.001

level. In comparison to the overall aggregate importance rating of 13.12, the rating for the Latin-

American cluster of 26.82 is the highest among cultural clusters, indicating that the importance

rating for the leadership attributes for staff is greater for the Latin-American cluster than for any

other cluster. The same can be said for Seniors/Supervisors (F¼12.61, p , 0.001), for whom 36.94

percent of Latin-American CAEs selected leadership attributes as most important, which is

significantly greater than the overall aggregate rating of 22.56. For managers, Latin-American

CAEs assigned the highest rating of 56.24, while the CAEs from Germanic-Europe (33.98)

TABLE 4

Factor Loadings for the Most Important Performance AttributesIdentified by CAEs for Staff

Attribute

Factor

1 2

Technical Skills Leadership Attributes

Confidentiality 0.579 0.005

Governance and ethics sensitivity 0.128 0.362

Interpersonal skills 0.541 �0.211

Leadership 0.081 0.574

Objectivity 0.643 �0.007

Team player 0.465 �0.178

Working independently 0.407 �0.244

Work well with all levels of management 0.286 0.237

Data collection and analysis 0.701 �0.136

Identifying types of controls (e.g., preventative, detective) 0.478 0.001

Interviewing 0.534 �0.106

Negotiating 0.153 0.498

Risk analysis 0.251 0.536

Understanding business 0.400 0.211

Ability to promote the internal audit function 0.121 0.539

Analytical (ability to work through an issue) 0.609 �0.067

Communication 0.497 0.028

Writing skills 0.476 �0.094

Variance Explained 20.21% 8.70%

12 Abdolmohammadi


assigned the lowest ratings. Overall, these differences were highly significant (F¼8.11, p , 0.001).

A similar result is observed for the importance rating of leadership attributes for CAEs, where the

Germanic-Europe cluster rating is the lowest (67.18) and the Latin-American cluster is the highest

(75.06) as compared with the overall rating of 71.89 (F ¼ 3.25, p ¼ 0.012).

For technical skills, no significant difference between cultural clusters was indicated for Senior/

Supervisors (F¼ 1.49, p¼ 0.202), and the difference for managers was significant at the marginal

level of 0.064 (F¼ 2.22). However, the differences in importance ratings by cultural cluster were

significant for staff (F ¼ 9.26, p , 0.001) and CAEs (F ¼ 2.86, p ¼ 0.023). The result for staff

indicates that the East-European rating (51.42) is significantly below the overall mean of 60.63.

Finally, the importance ratings of technical skills for CAEs in the East-European cluster (50.85) are

significantly greater than the overall rating of 46.21.

In summary, Table 6 shows that while Anglo-Saxon and Latin-European CAEs’ ratings did not

differ significantly from the overall ratings, East-European, Germanic-Europe, and Latin-American

FIGURE 1Attribute Importance by Professional Rank

TABLE 5

Leadership Attributes versus Technical Skills(Mean Proportions of CAEs Selecting Attributes with Their Standard Deviations

and Rank Orders)

Staff Sr/Supr IAM CAE Kendall W-Statistic

a. Leadership Attributes 13.12 22.56 42.18 71.89

(Standard Deviation) (17.49) (23.17) (30.87) (25.64)

Mean Rank 1.58 1.95 2.74 3.73 0.659a

b. Technical Skills 60.63 41.91 35.37 46.21

(Standard Deviation) (24.29) (27.18) (24.17) (18.54)

Mean Rank 3.43 2.44 1.80 2.32 0.333a

a Significant at the 0.001 level.



clusters differed from overall ratings for some of the professional ranks. In a search for the source of

these variations, an analysis by cultural cluster was performed for each of the five attributes in the

leadership attributes factor for each of the professional ranks. The results are reported in Table 7,

where significant differences are bolded. In the discussion that follows, two points about the

findings in Table 7 are summarized. The first is the degree of cultural cluster effect on the results,

where the v2 test and its significance are reported for each attribute. The second point is that the

cultural cluster that deviates the most from the overall importance ratings of attributes in either

direction is bolded.

Panel A of Table 7 shows that all of the five leadership attributes for staff are significantly

different by cultural clusters (i.e., all v2 tests are highly significant). As to the details of these

differences, Table 7 shows that while the Anglo-Saxon ratings are not different from the overall

ratings, other cultural clusters have some differences. For example, East-European CAEs rated

governance and ethics at a higher level (57 percent) than other clusters, followed by Latin-

American (46 percent) and Latin-European (30 percent) clusters. Differences were also observed

for seniors/supervisors, IAMs, and CAEs. However, the number of significant v2s decreases for

higher ranks (i.e., more consensus is observed for higher ranks than for lower ranks). For example,

of the five attribute ratings for CAEs, four v2s were not significant. Overall, 15 of the 20

comparisons (five attributes times four professional ranks) were significant. This result provides

general support for H1 at the individual attribute level.

TABLE 6

Performance Attribute Factor Ratings by Cultural Cluster(Proportions of CAEs Selecting the Attributes within Each Factor)

FactorAnglo-Saxon

n ¼ 824

East-European

n ¼ 54

GermanicEuropen ¼ 103

Latin-American

n ¼ 85

Latin-Europeann ¼ 263

Overalln ¼ 1,329a

F-Statistic(Significance)

Leadership Attributes of:

Staff 10.17 19.26 12.82 26.82 16.81 13.12 25.20(, 0.001)

Sr./Supr. 20.15 18.52 23.88 36.94 25.78 22.56 12.61(, 0.001)

Manager 40.85 37.41 33.98 56.24 46.01 42.18 8.11(, 0.001)

CAE 71.09 68.52 67.18 75.06 75.89 71.89 3.25(0.012)

Technical Skills of:

Staff 63.55 51.42 59.60 56.74 55.02 60.63 9.26(, 0.001)

Sr./Supr. 42.63 34.19 41.82 43.89 40.63 41.91 1.49

(0.202)

Manager 35.88 34.05 28.83 37.47 35.92 35.37 2.22(0.064)

CAE 45.75 50.85 42.20 48.42 47.53 46.21 2.86(0.023)

Bold numbers are significant at the 0.05 level or lower.a The decrease in sample size to 1,329 observations from the original sample size of 1,497 observations is due to missing

data.

14 Abdolmohammadi


The bolded largest deviations from the overall rating for each of the leadership attributes in

Table 7 indicate that Anglo-Saxon CAEs’ ratings do not differ from the overall ratings for any of

the 20 attribute comparisons (i.e., five attributes times four professional ranks). The next lowest

difference is observed for the Latin-European cluster with only one deviation (indicating higher

importance rating for governance and ethics sensitivity for staff ).

Table 8 presents the same analysis (as in Table 7) for the 13 technical skills. The results

indicate that while 11 of the 52 v2s (13 attributes times four professional ranks) are not significantly

different across cultural clusters, the remaining 41 are. This result provides further support for H1 at

the individual attribute level. Compared to the overall results, the Anglo-Saxon cluster does not

show differences, while Latin-European shows five differences. The East-European cluster has 19

significant differences, while Latin-American cluster has the highest at 13 differences, followed by

Germanic-Europe with 10 differences.

Additional Analysis

To investigate whether demographic variables had any effects on the main results, four logistic

regressions (one per each professional rank) for each of the 18 attributes perceived as being most

important by CAEs were performed for a total of 72 regressions. In each of the regressions, the

selection of the attribute by CAEs was used as a binary dependant variable (yes/no). Independent

variables were cultural cluster, where dummy variables were used for each cluster (cluster 1/other,

cluster 2/other, etc.) and the demographic variables listed in Table 2 (except for the size of the

organization that was highly correlated with the size of the internal audit function). The results of

these numerous logistic regressions (not tabulated) provide support for the robustness of the

findings reported earlier because the Logits typically returned statistically significant models with

pseudo R2s of less than 10 percent, where at least one of the dummy variables for cultural cluster

was statistically significant. Also, while there were isolated significant effects in some of the logistic

regressions for some of the demographic variables (e.g., CAE age, CAE education level), they did

not present a recognizable pattern of effects on CAE responses.

The significant differences by rank and cultural cluster, as reported earlier, suggest that there

might be an interaction effect between cultural cluster and professional rank. An interaction effect

would suggest that CAE choice of most important attributes for each professional rank is affected

by the cultural cluster to which the CAE belongs. To test this interaction formally, I performed

Logit regressions in which CAE selections of each of the 18 attributes (yes/no) were regressed

against binary cluster variables (cluster 1/other, cluster 2/other, etc.), the professional rank variable

(1–4 for staff, senior/supervisor, manager, and CAE), and their interactions. The results (not

tabulated) indicate that all logistic regressions produce highly significant v2 values at the p , 0.001

level. In all but one of these 18 regressions, at least one (and at most three) of the four cluster/rank

interactions was significant.4 The ‘‘confidentiality’’ regression model was the exception in which the

overall model was statistically significant, but none of the interactions between cultural cluster and

professional rank was significant. The significant interaction effects in 17 of the 18 models add

further support to the conclusion that cultural cluster is associated with significant differences in the

CAE assessments of attribute importance for various professional ranks.

Related to cultural cluster analysis is an observation in Table 2 indicating that CAEs in the

East-European cluster had the lowest age, experience (both years as CAE and years of internal audit

experience), and length of membership in the IIA than other four clusters. The East-European CAEs

4 The breakdown of interactions was as follows: 15 Cluster-1/rank interactions, 8 Cluster-2/rank interactions, 11Cluster-3/rank interactions, and 2 Cluster-4/rank interactions. Adding Cluster-5 (yes/no) and its interaction withrank would be redundant to the model specification.



TABLE 7

Leadership Attributes by Cultural Cluster(Proportions of CAEs Selecting Each Attribute in Each Cultural Cluster)

Panel A: Staff

Anglo-Saxon

n ¼ 824

East-European

n ¼ 54


Latin-American

n ¼ 85


Overalln ¼ 1,329a v2 (Sig.)

Governance and

Ethics Sensitivity

15 57 18 46 30 22 103.81(0.000)

Leadership 2 0 1 14 2 3 46.15(0.000)

Negotiating 6 9 13 7 9 7 9.74(0.045)

Risk Analysis 22 30 23 49 34 26 42.64(0.000)

Ability to promote IA 7 0 10 18 9 8 17.23(0.002)

Panel B: Senior/Supervisor

Anglo-Saxon

n ¼ 824

East-European

n ¼ 54


Latin-American

n ¼ 85



Governance and

Ethics Sensitivity

15 28 24 38 28 20 42.62(0.000)

Leadership 19 13 18 39 20 20 21.22(0.000)

Negotiating 22 17 39 27 24 24 15.77(0.003)

Risk Analysis 37 33 33 59 48 40 27.33(0.000)


Panel C: Manager

Anglo-Saxon

n ¼ 824

East-European

n ¼ 54


Latin-American

n ¼ 85



Governance and

Ethics Sensitivity

21 24 27 48 43 28 70.40(0.000)

Leadership 49 44 31 61 45 47 19.07(0.001)

Negotiating 50 43 40 55 50 50 6.34

(0.175)

Risk Analysis 60 52 47 68 61 59 11.34(0.023)



16 Abdolmohammadi


also had the lowest percentage of graduate studies and professional certification than other clusters.

Given these differences, I repeated the cultural difference analysis in Table 6 without the East-

European cluster to see if the inter-cultural differences reported earlier hold for the remaining four

clusters. The results (not tabulated) were fully consistent with those reported in Table 6.

Specifically, all ‘‘Leadership Attributes’’ were highly significant by cultural cluster. The same was

true for technical skills, where the intercultural difference was not significant for seniors/

supervisors, but the remaining three ANOVAs were statistically significant.

As Table 1 shows, sample sizes from Poland and Venezuela were in the single digits, causing

concern about their sample limitations. Consequently, I dropped these 2 countries from the overall

sample and analyzed the data based on the remaining 17 countries. With one exception, the results

mirrored those reported in Table 6. The exception was that while the cultural cluster was significant

at the 0.023 for CAE technical skills in Table 6, it was not significant in the analysis of the 17

countries that excluded Poland and Venezuela (p ¼ 0.125).

The next analysis performed was on the effects of industry on CAE choices. Abdolmohammadi

et al. (2004) argue that industry effect analysis has implications for the training and assignment of

industry specialists to audit tasks. For example, depth of technical skills may be more important to

industries with more likelihood of litigation (e.g., product safety in manufacturing) than those with

less likelihood of litigation (e.g., utilities). While CBOK (2006) has data on 22 industries, sufficient

sample sizes were available for only 10 industries (banks, education, finance, health, insurance,

manufacturing, retail, technology, transportation, and utilities) in the current study for meaningful

statistical analysis (i.e., at least ten observations per professional rank). CAE performance attribute

ratings were analyzed by these 10 industries first, and then by more-regulated (e.g., banks, utilities)

versus less-regulated (e.g., manufacturing, retail) industry classification. The results (not tabulated)

do not present a clear pattern of industry effects. For example, the more-regulated/less-regulated

industry analysis indicated significance for staff auditors in only 2 of the 18 performance attributes.

These differences do not support broad generalizations.

TABLE 7 (continued)

Panel D: CAE

Anglo-Saxon

n ¼ 824

East-European

n ¼ 54


Latin-American

n ¼ 85



Governance and

Ethics Sensitivity

48 37 50 71 63 52 35.67(0.000)

Leadership 80 78 70 80 75 78 7.16

(0.128)

Negotiating 73 69 68 67 77 73 5.66

(0.226)

Risk Analysis 78 74 70 75 82 78 6.96

(0.138)

Ability to promote IA 77 85 79 82 82 79 5.41

(0.248)

a The decrease in sample size to 1,329 observations from the original sample size of 1,497 observations is due to missingdata.

Bold data highlight significance.



TABLE 8

Technical Skills by Cultural Cluster(Proportions of CAEs Selecting Each Attribute in Each Cultural Cluster)

Panel A: Staff

Anglo-Saxon

n ¼824

Eastern-Europen ¼ 54

Germanic-Europen ¼ 103

Latin-American

n ¼ 85



Confidentiality 70 67 76 84 81 73 18.87(0.001)

Interpersonal skills 76 56 58 54 61 69 46.06(0.000)

Objectivity 74 63 73 84 75 75 7.69

(0.104)

Team player 66 69 84 72 68 67 1.84

(0.765)

Work independently 62 57 70 34 43 57 54.26(0.000)

Work well with management 46 15 38 33 39 42 25.30(0.000)

Data collection and analysis 78 78 77 79 77 78 0.14

(0.989)

Identify types of controls 60 24 35 64 48 55 53.25(0.000)

Understanding business 48 35 59 39 33 45 30.05(0.000)

Interviewing 63 57 60 44 57 60 14.31(0.006)

Analytical 70 50 68 59 57 66 22.36(0.000)

Communication 58 43 52 41 30 50 70.60(0.000)

Writing skills 55 56 45 53 46 52 9.06(0.060)

Panel B: Senior/Supervisor

Anglo-Saxon

n ¼ 824



Latin-American

n ¼ 85





Objectivity 52 44 62 58 59 54 9.32(0.054)

Team player 31 37 36 55 42 35 28.38(0.000)



18 Abdolmohammadi


TABLE 8 (continued)

Anglo-Saxon

n ¼ 824



Latin-American

n ¼ 85





(0.23)



Interviewing 45 32 52 38 49 45 9.95(0.041)

Analytical 47 37 52 55 45 47 6.44

(0.169)

Communication 46 32 40 38 32 42 18.82(0.001)

Writing skills 39 32 27 35 27 34 10.93(0.027)

Panel C: Manager

Anglo-Saxon

n ¼ 824



Latin-American

n ¼ 85





Objectivity 41 44 44 55 47 43 8.53(0.074)

Team player 12 26 11 39 27 17 6.34

(0.175)

Work independently 13 22 16 18 19 15 7.71

(0.103)



(0.141)

Identify types of controls 32 41 29 38 38 33 6.63

(0.157)


Interviewing 37 33 31 25 42 36 10.35(0.035)

Analytical 28 43 21 39 31 29 12.83(0.012)

Communication 50 19 37 44 44 46 25.13(0.000)

Writing skills 28 19 11 27 21 25 20.55(0.000)




SUMMARY AND CONCLUSIONS

A large sample of chief audit executives (CAEs) from 19 countries located in five different

cultural clusters identified the most important performance attributes for the practice of internal

auditing. The results show that while leadership attributes increase in importance by professional

rank, technical skills generally decrease in importance by professional rank. The results also

indicate that importance of performance attributes differs by cultural cluster. Robustness of the

main results was confirmed through various multivariate analyses, where significant interaction

effects between cultural cluster and professional rank were found. However, industry-specific

analysis indicated no pattern of industry differences for the vast majority of performance attributes.

These results suggest that a generic profile for internal auditors, regardless of industry, may be in

order. However, for a small minority of the attributes for which industry may have effects,

industry-specific guidance may be appropriate. This conclusion suggests future studies of

industry-specific effects for the purpose of developing industry-specific guidance. The IIA’s (2009)

TABLE 8 (continued)

Panel D: CAE

Anglo-Saxon

n ¼ 824



Latin-American

n ¼ 85





Objectivity 51 69 63 64 64 56 22.84(0.000)

Team player 9 24 7 35 22 14 5.66

(0.226)



Data collection and analysis 11 33 12 20 14 13 25.38(0.000)



Interviewing 39 33 34 26 40 36 6.32

(0.176)

Analytical 21 39 21 37 27 24 19.34(0.001)

Communication 67 35 44 59 62 62 38.66(0.000)

Writing skills 26 37 13 26 19 24 17.11(0.002)

a The decrease in sample size to 1,329 observations from the original sample size of 1,497 observations is due to missingdata.

Bold numbers are significant at the 0.05 level or lower.

20 Abdolmohammadi


Framework has no industry-specific guidance, and it has indicated that such information will be

added when available.

An interesting finding in the study is that attributes such as financial analysis, research skills,

and statistical sampling that have theoretical appeal to the practice of internal auditing were not

selected by the CAEs as most important attributes. This result may be an artifact of limiting the

selection of the attributes to the top five from each category of behavioral, technical, and

competencies. The differences may also be due to the effects of culture. For example, research skill

was selected by only 45 percent of CAEs for staff, and even lower percentage for seniors/

supervisors, IAMs, and CAEs. However, an analysis by culture indicated that this attribute was

selected by more than 50 percent of CAEs in East-European and Germanic-Europe clusters. These

differences suggest that future studies may be needed to use scales (e.g., Likert scale, rank ordering)

other than forced selection of the top five attributes from each category to assess the importance of

all attributes, not just those assessed as the most important.

Another interesting result in the study is that ‘‘governance and ethics sensitivity’’ was rated to

be more important for staff in Eastern Europe, but more important in Latin America for the other

professional ranks. This evidence suggests that more research may be needed to investigate this

attribute further to find more specific reasons for the differences by cultural cluster. For example,

future research may benefit from investigating the association of specific cultural dimensions, such

as uncertainty avoidance and power distance, with importance ratings of various performance

attributes.

The IIA’s (2009) Internal Auditor Competency Framework was issued to provide guidance on

detailed performance attributes and called for research data to develop the Framework further.

According to the IIA (2009), the highly detailed competencies in the Framework represent the

‘‘desirable’’ and the ‘‘ideal.’’ The current study contributes evidence on some of the important

attributes in the Framework. Additional research may be needed to investigate further these and

other performance attributes to refine the Framework further.

A limitation in the current study is CBOK’s (2006) use of the survey method to collect data.

While CAEs have experience and insight about the subject matter of the current study, the results

may, nevertheless, suffer from the general limitation of the data being perceptual in nature. It would

be desirable to access actual personnel files to assess the importance of the attributes on actual

performance evaluations of internal auditors by their organizations. While privacy laws may make

such a study difficult to conduct, confidential access to a limited number of cases can provide

insight about actual internal audit performance attributes in specific industries.

Another limitation of the study is related to the size of the internal audit function and the size of

the organization as a whole. The data in the bottom of Table 2 indicate that 79.5 percent of the

organizations represented in the current study had 12 or less professionals in their internal audit

functions. These data raise a question as to whether the results of the current study generalize to

larger organizations. Future research using large organizations is needed to further investigate this

potential difference.

Two theoretically appealing attributes, ‘‘financial analysis’’ and ‘‘understanding complex

information systems,’’ did not receive support from CAEs as most important performance attributes

of internal auditing. This finding is surprising in the sense that financial analysis is regularly

performed by internal auditors. Also, since information systems are at the core of the contemporary

enterprise, internal auditors need to understand this technology well. A recent survey by Ernst and

Young (cf., Update 2009) finds that technology plays a growing role in internal auditing. However,

Update (2009) also finds that most organizations do not develop these competencies; instead, they

outsource them. Future research may be needed to investigate the exact level of competency in

information technology (and financial analysis) that is needed for the practice of internal auditing by

internal auditors of varying professional rank, and how to acquire this competency.



Finally, the results of this study have implications for behavioral research. Specifically, the

results provide detailed performance attributes that can assist behavioral researchers in the

development and measurement of performance attributes. By knowing the performance attributes

that are important to internal auditors, behavioral researchers can engage in research on

development of training programs and decision aids that can improve, assist, or reinforce

performance attributes. Likewise, detailed performance attributes could be subjected to behavioral

research aimed at improving training programs to assist professionals become more effective in

their performance.

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