Assurance Practitioners’ and Educators’
Self-Perceived IT Knowledge Level: An
Empirical Assessment
Marilyn Greenstein
Arizona State University-West
marilyn.greenstein@asu.edu
Thomas E. McKee
East Tennessee State University
temckee@mail.etsu.edu
October 3, 2003
Assurance Practitioners’ and Educators’ Self-Perceived IT
Knowledge Level: An Empirical Assessment
Abstract
In September 2001, the IFAC Education Committee issued Exposure Draft IEG1 which states “Information technology is pervasive in the world of business.
Competence with this technology is an imperative for the professional accountant.” This
view was seconded by a leading visionary in the assurance profession who recently
stated “Every aspect of the accounting profession is being pervasively affected by
advances in information technology.” (Elliott, 2002) Clearly, accounting/auditing
education needs to incorporate these technology changes in order to stay relevant.
Despite the apparent need for change, leading academicians have recently talked about
the possible demise of accounting/auditing education due to a failure keep up with
changes in the business world. Accounting/auditing education has been described as
outdated, broken, and in need of significant modification. (Albrecht and Sack, 2000).
This study focuses on identifying appropriate information technologies for auditing
professors and audit practitioners and measuring their self-perceptions of knowledge
about these technologies.
We conducted a literature search that resulted in the identification of thirty-six
critical information technologies. We then surveyed 1,000 accounting information
systems and auditing professors and 1,000 audit practitioner to determine their selfreported IT knowledge levels and perceptions about the best places to learn IT skills,
The survey also solicited their views about the best place in the educational process for
initially learning these technologies as well as views about educational methodology.
Response rates of 31.2% for professors and 24.7% for practitioners were obtained for
the survey.
After conducting factor analysis, we found a relatively low level of knowledge for
e-commerce and advanced technologies and audit automation constructs by both
educators and practitioners, but we found a relatively high level of knowledge for office
automation and accounting firm office automation constructs. Results also indicate that
the educators with more teaching experience, but lower reported IT knowledge levels,
tend to teach auditing. Further, we found a potential “learning gap” between educators
and practitioners that may occur for five of the 36 technologies examined.
The results of this research are important for auditing and AIS education since
they strongly suggest that more attention needs to be paid to issues such as assigning
courses based on knowledge level rather than seniority, technology training and
awareness programs for educators and practitioners, and coverage of information
technology in the university curriculum.
Key Words: Auditing, Assurance, Accounting Information Systems, Information
Technology Knowledge,
Data Availability: Contact the authors regarding data availability.
Assurance Practitioners’ and Educators’ Self-Perceived IT
Knowledge Level: An Empirical Assessment
INTRODUCTION
In September 2001, the IFAC Education Committee issued Exposure Draft IEG-1
which states “Information technology is pervasive in the world of business. Competence
with this technology is an imperative for the professional accountant” (IFAC, 2001, p. 6)
This view was seconded by a leading visionary in the auditing profession who recently
stated “Every aspect of the accounting profession is being pervasively affected by
advances in information technology.” (Elliott, 2002, p. 139) It is also supported by the
audit practice of the “Big Five” audit firms as indicated by the following comment,
“Technology is embedded in everything we do during the audit process, similar to the
practices of our clients.” (Winograd, Gerson, and Berlin, 2000, p. 179) Technology was
identified in a recent major accounting education study as one of three change drivers
that has heavily impacted modern business and caused the traditional accounting model
to be outdated. (Albrecht and Sack, 2000, pp. 5,13)
Albrecht and Sack [2000, p.1]
state that “… accounting education today is plagued with many serious problems and
our concern is that if those problems are not seriously addressed and overcome, they
will lead to the demise of accounting education.” They further assert that “accounting
education is not changing fast enough to keep up with changes in the business world…”
and that “increasingly more of the knowledge, skills and abilities needed to do what
‘accountants’ do are being taught by other…disciplines” (Albrecht and Sack, 2000, p.
39). Clearly, in order to stay both relevant and suitably connected to audit practice,
auditing education needs to incorporate appropriate information technology.
A lack of knowledge exists about AIS and audit educators’ attitudes toward IT,
whether they believe they have adequate IT skills and which institutions (pre-college,
college, or accounting practice) they believe can best provide education about those
technology skills. Another important issue is the perceived value of various IT skills by
audit practitioners and where they believe these skills should be learned. Agreement
between educators and practitioners as to the importance and value of IT skills and
where they should be taught is important to ensuring that accounting/auditing students
are being adequately prepared for their accounting careers. Appropriate knowledge of
these issues and the profession’s attitudes about them is critical for formulating
appropriate policies and plans for the future.
This paper presents the results of research designed to identify what
accounting/auditing technologies are considered to be critical for the current business
environment and investigate knowledge and attitudes of auditing and accounting
information systems (AIS) professors and audit practitioners about the identified
information technologies. It also reports opinions about the current roles colleges,
universities, and accounting firms should play in teaching AIS and auditing information
technologies.
This research provides insights into issues with which educators, educational
institutions, accounting/auditing firms, and government might be concerned in advancing
information technology knowledge in the U.S.’s higher education and the accounting
profession.
Literature Review
The International Federation of Accountants (IFAC) looked at the significance of
IT in the accounting curriculum. In the December 1995 IFAC Education Committee
Guideline 11, they state “Competence with this technology is an imperative for
professional accountants.” They also state information technology “… requires special
attention due to its explosive growth and its rapid rate of change.”(IFAC, 1995, p.1-2)
This conclusion is reinforced by the European Union’s White Paper on Education and
2
Training which asserts, “Expansion of technical innovation in all areas has generated
new knowledge requirements.” (European Commission, 1996, p. 5) The IFAC recently
issued an Exposure Draft entitled, Information Technology for Professional Accountants.
(IFAC, 2001, p. 6) In this exposure draft, the IFAC underscores the need for accountants
to become technologically proficient. The Exposure Draft states “Information technology
is pervasive in the world of business. Competence with this technology is an imperative
for the professional accountant.” The exposure draft identifies IT knowledge and
competency areas for professional accountants, that are considered core as well as by
role. The four roles are identified as: Users of IT, Manager of Information Systems,
Designer of Information Systems, and Evaluator of Information Systems.
Traditional assurance services are evolving as the underlying accounting
processes change. The increasing electronization of businesses and underlying
processes is a phenomenon discussed by Vasarhelyi and Greenstein (2001). The
increasing techno-intensification of processes and its impact on auditor reliance on
automated vs. non-automated controls was explored by Viator and Curtis (1998). They
assert that as computer auditors need to increasingly assess the relative effectiveness of
automated control procedures, an important issue is knowing when reliance on these
controls is justified or not. They also note the importance of “characteriz(ing) the
appropriate educational preparation and work experience for computer auditors.” Elliott
(1998, p. 2) states that the AICPA Committee on assurance services “…made
suggestions on the need to adapt audit tools to the changes being created in information
technology.” He comments that demand factors will “…lead to eventual user access to
corporate databases with real-time assurance by auditors.” “Transforming financial
reporting in this way will necessitate a transformation in auditing.”
When we look beyond traditional assurance services to new assurance services,
the AICPA also recognizes the needs for accountants to acquire new skills; however, as
discussed by Greenstein and Ray (2001), many of their discussions are framed from the
3
perspective of delivering new services, such as systems assurance and electronic
commerce, rather than as necessary to perform the traditional assurance function for the
audit profession Elliott (1998, p. 5) supports this view by stating, “There is a close
relationship between the information-technology skill and knowledge that should be part
of the auditor’s toolkit and the skills and knowledge that will be applicable to a significant,
probably a high, proportion of nonaudit assurance services. The next section discusses
the process of identifying critical IT knowledge.
Critical IT Skill Identification
An ideal accounting education includes both conceptual knowledge and practical
skills in a wide range of information technologies. These knowledge and skills could
possibly be obtained in secondary school, college or university education, on-the-job
training, self-study, or formal continuing professional education programs by auditing
firms. The critical questions are:

What information technologies should be learned?

What is the skill level of members of the academic and practice fields for these
technologies?

Where should they be learned?
In this research project, we attempt to answer these questions by first identifying
critical technologies from prior literature and then examining educators’ and practitioners’
perceived skill level and opinions about where these technologies should be initially
learned. The next part of this section discusses the identification of the 36 IT skills used
in this study.
The IFAC draft report defines information technology as “…hardware and
software products, information systems operations and management processes, and the
skills required to apply those products and processes to the tasks of information
4
production and information systems development, management, and control” (2001, p.
9) Unfortunately, the IFAC draft report does not provide a framework for identifying or
categorizing these technologies nor does it provide a comprehensive technology listing.
A detailed literature search also did not reveal either a framework for identifying
or categorizing current relevant information technologies for assurance practitioners and
educators nor a comprehensive information technology listing that was relevant for
assurance practitioners and educators. Accordingly, one purpose of this study was to
develop a comprehensive listing of information technologies perceived to be relevant for
assurance practitioners and educators.
A technology listing was developed via a detailed literature search in which the.
following particularly relevant sources were identified:
the AICPA’s Auditing
Procedures, “Auditing with Computers,” published in 1994; a 1994 article by Elliott; a
1997 article by Helms and Mancino; a 1997 article by Glover and Romney; the AICPA’s
1997 and 2000 Top 10 Technologies lists; a 2000 article by McKee; and a 2000
electronic commerce book by Greenstein and Feinman. These sources were then used
to develop 36 key assurance related information technologies.
Table 1 lists the critical skills identified via this literature search. The related
definitions were, when possible, abstracted from the original source. However, in some
cases, skills were listed by the original source, but not defined. In such cases,
definitions were taken from other sources, such as IFAC Guideline 11 and Greenstein
and Feinman, 2000.
Several of the identified technologies are interrelated or incorporate the same
functions, so these technologies are not necessarily completely distinct technologies.
For example, electronic spreadsheets typically have data import/export capability that
may provide the same data handling capability for certain file types as a generalized
audit software package. However, we believe the technologies to be distinct enough to
warrant listing them separately for purposes of this research.
5
RESEARCH QUESTIONS
In this study, we examine whether accounting educators and practitioners in the
U.S. perceive themselves as being knowledgeable of relevant, current information
technologies. As previously discussed, the need to examine this issue is crucial. Thus,
in this study, we explore the following two research questions about technology skill
levels.
H1: The perceived knowledge of AIS and auditing educators includes relevant,
current information technologies.
The first hypothesis assesses the perceived capability of accounting educators to
facilitate transfer of knowledge about relevant, current information technologies to
students.
H2: The perceived knowledge of auditing practitioners includes relevant, current
information technologies.
The second hypothesis assesses the perceived capability of practitioners to use
knowledge about relevant, current information technologies to better service their clients.
We are also interested in examining the relative perceived skills levels of the
academicians and practitioners, and this leads us the following hypothesis:
H3: AIS and auditing educators have the same perceived knowledge of relevant,
current information technologies as audit practitioners.
Also of interest is the assessment by both practitioners and educators regarding
where such skills sets should be learned before entering practice. This leads us to
formulate the following hypotheses:
H4: AIS and auditing educators believe that relevant, current information
technologies should be initially learned before entering practice.
H5: Audit practitioners believe that relevant, current information technologies
should be initially learned before entering practice.
6
The fourth hypothesis examined is important in knowing whether accounting educators
that are in a position to influence and change or update accounting curriculums believe
that relevant, current information technologies should initially be learned before the
student graduates. Further, the fifth hypothesis is important in assessing whether
practitioners will value such skill sets by new recruits. Finally, we examine whether the
educators and practitioners agree on where such skill sets should be learned:
H6: AIS and auditing educators agree on the best place to initially learn relevant,
current information technologies.
In assessing the sixth hypothesis, we are particularly interested in identifying any
learning gaps, such as where educators may believe that the best place to learn a
specific skill is during practice, while the practitioners believe that the best place to learn
the skill is before entering practice.
METHODOLOGY
An overview of the methodology and the hypothesis tested are illustrated in
Figure A. After performing a literature search to identify thirty-six critical IT knowledge
areas, a survey instrument incorporating these thirty-six IT knowledge areas was
designed, tested, and administered. Each of these activities is discussed below, and the
following section discusses the data analysis and results.
Survey Instrument Design
In order to examine the hypotheses formulated in the previous section, a survey
instrument was designed to gather information about both current and possibly useful
future AIS and audit technologies. The previously described process identified thirty-six
information technologies that have the potential to significantly impact professional audit
practice. These technologies were included in the survey instrument.
7
A survey instrument used by McKee (2000) was adapted for this study. McKee’s
instrument measured the knowledge level of 25 of the 36 technologies identified for this
study. Eleven additions were made to McKee’s survey instrument to ensure that all of
the 36 IT skills identified in the literature were incorporated into the instrument:
For each of the 36 technologies, the respondents were asked to respond with two
pieces of information:

What is the extent of your personal knowledge of each of the information
technologies?

Where is the single best place to initially learn each of the technologies?
A seven point scale was used to assess the first question. This scale varied from 1No Knowledge to 7- Expert Knowledge. Four options were available to the respondents
regarding the second questions: No Opinion; Before College or University; College or
University; and Accounting/Audit Practice.
In addition to assessing these 36 technologies on the two dimensions mentioned
above, the following questions were asked:

Which “three technologies would you MOST like to know more about?”

What proportion of theory versus practical applications would be BEST for
teaching information technologies in a college or university setting?

How would you assess the overall coverage of information technologies in your
university, college, and department?

How would you assess your own overall knowledge of information technology?

What is your opinion about the overall level of use of information technology by
professional auditing firms?
Demographic questions were also included in the survey. For the educators, these
include gender, age, courses taught, and number of years teaching experience in the
AIS, audit, and/or e-commerce area. For the practitioners, these include gender, age,
type of firm, position, and area of specialty.
8
Survey Samples of AIS and Auditing Educators and Audit Practitioners
As mentioned previously, the primary objective of this study is to determine the
current technology knowledge of AIS and auditing educators and practitioners and their
perceptions about the possible roles of various institutions in the educational process.
Respondents were assured confidentiality. The practitioner questionnaires were pretested by two accounting professionals at Big Five firms, and the educator
questionnaires were pre-tested by two accounting professors. Pre-testing resulted in
only minor wording changes to improve some technology descriptions.
After successfully pre-testing, the survey instrument was sent to 1,000 AIS and
auditing instructors and 1,000 audit practitioners. The educator sample of 1,000
professors was randomly selected from the population of members of the following three
sections of the American Accounting Association: Artificial Intelligence/Emerging
Technologies, Auditing, and Information Systems. Because members can join more
than one section, duplicates were removed. Also, all non-academics and international
members were excluded from this sample. The latter because concurrent research
projects are being conducted in other countries with the objective of making inter-country
comparisons. Thus, the group surveyed is a U.S. sample of strictly academicians.
The sample of 1,000 practitioners was selected from the AICPA’s database of
accountants that are classified as either auditing, internal auditing, or governmental and
auditing. Further, since prior research on information technology knowledge in Norway
suggested gender specific knowledge issues, the sample was selected with a 50-50 split
between males and females.
This split was made to insure that enough responses in
each category would be received to analyze gender related issues.
9
RESEARCH RESULTS
An initial mailing was made during March, 2000. A second mailing to nonrespondents was made about four weeks after the first mailing for the educators and
seven weeks for the practitioners to avoid their busy season. The response rates are
presented in Table 2. The gross response rate for educators was 32.8% and the
adjusted response rate was 31.2%. The gross responses rate for practitioners was
24.9% and the adjusted response rate was 24.7%.
A comparison was made between early respondents and late respondents to
gauge the nature of possible non-response biases. This analysis, which is discussed
later in this paper as part of the research limitations, indicated a mild non-response bias.
Individuals who were interested in and knowledgeable about information technology
were slightly more likely to respond to the survey instrument.
Demographic Response Information
The questionnaire contained a number of questions concerning the background
of respondents. Responses for the overall survey are presented in Table 3 and are
highlighted and discussed below.
Age: Educator respondents indicate that their ages ranged from 32 years to 76
years with a mean age of 49 years. Practitioner respondents indicate that their ages
ranged from 23 years to 72 years with a mean age of 42 years.
Gender: Educator respondents indicate that 76 (25%) are females and 223
(73.4%) are males (5 (1.6%) did not indicate gender). Practitioner respondents indicate
that 146 (59.4%) females and 96 (39%) males responded, while four individuals (1.6%)
did not indicate their gender. For the educators, we cannot ascertain whether the
respondent population is representative of the sample population since we did not collect
gender data from the AAA list. For the practitioners, however, proportionately more
10
females than males responded. Using a Chi-squared test to compare the respondent
ratio with the 50-50 sample ratio, the difference is statistically significant at .01. The
potential impact of this response bias is discussed later in the results section.
Experience. Educator respondents’ experience ranges from 0 to 37 years with a
mean experience level of 12.3 years. Practitioner respondents’ experience ranged from
0 to 51 years with a mean experience level of 16.6 years.
Educators’ Teaching Areas: The educators surveyed were asked to indicate
whether or not they teach AIS, audit and/or e-commerce. The results indicate that the
majority (65%) of respondents teach only one of the three subjects. Those educators
teaching AIS only represented 68 (22%) of the respondents and 130 (43%) represented
those teaching audit only. Those educators teaching both AIS and Audit represented 53
(17%) of the sample, and those educators teaching e-commerce, usually in conjunction
with some other course, represented 26 (9%) of the sample. Although exploring
differences between educators of the three subjects is not an explicit research objective
of this study, they are examined in the analysis.
Practitioners’ Firm Type and Position: The practitioners were asked to indicate
whether the firm for which they work performs traditional audits. The responses indicate
that 103 (42%) work for firms that perform traditional audits, while 141 (57%) do not.
Checks are made later in the analysis to determine whether responses differed by
respondents between these two groups. Further demographic data was collected for
those 103 firms that perform traditional audits and is presented in Table 3. The
respondents indicate that 51% of this group are at the partner level and 23% are at the
manager level, thus indicating a high level of expertise in their respective areas.
Thus, the demographic data indicates that both the educators and practitioners
responding to the survey responses varied widely in age, teaching experience, and
teaching area, and on average, are quite experienced in their respective career
positions.
11
Response Variable Correlations
Tables 4 and 5 list all significant correlations between demographic variables and
the perceived knowledge ratings for the 36 IT skill items. For the educators, age and
gender (1=female, 2=male) are statistically, positively correlated. For the practitioners,
age and gender are also statistically, positively correlated as well as experience and
gender.
Age is significantly, negatively correlated with word processing and electronic
spreadsheets for both educators and practitioners. For educators, age is also
significantly, negatively correlated with Internet search and retrieval and cooperative
client/server environment. For practitioners, age is also significantly, negatively
correlated with e-mail and groupware. One possible explanation of these practitioner
findings is the use of such technical tools are considered by many older workers to be
tasks that staff/support workers would use rather than individuals at the managerial level
or higher.
For educators, gender is significantly, positively correlated with 5 of the 36 IT
knowledge levels: meaning that male professors report higher knowledge levels for
electronic working papers, generalized office software, simulation software,
flowcharting/data modeling, and user authentication systems. For practitioners, gender
is significantly, positively correlated with 23 of the 36 technologies. A list of these 23
technologies can be found in Table 5. Further analysis is conducted later to examine
whether gender is still a significant factor after considering experience.
Experience is significantly positively correlated with 12 of the 36 technologies for
the educators and for 6 of the 36 technologies for practitioners. Interestingly, very little
overlap exists in these positive correlations between educators and practitioners. The
only overlapping items are EDI-traditional and application service providers. Two
significant, negative correlations with experience are found for educators: word
processing and electronic spreadsheets, consistent with the age finding for this group.
12
One significant, negative correlation with experience is found for practitioners:
groupware. This finding is also consistent with one of the age findings for this sample
group.
Information Technology Knowledge
The first part of the questionnaire asked respondents to rank their own
knowledge of 36 information technologies on a 1 to 7 scale where 1 = No Knowledge
and 7 = Expert Knowledge. The responses were sorted into descending order based
on the means and are listed in Table 6 for the academicians and Table 7 for the
practitioners.
Factor Analysis of the 36 Technology Skills
The 36 items listed in Table 1 were factor analyzed on the perceived skill level response
for the 36 information technologies to determine the number and character of underlying
constructs. After Varimax rotation, the analysis revealed five factors with eigenvalues
greater than one, which accounted for 68 percent of the variance (see Table 8). Five
technologies did not load strongly on any one of the constructs: expert systems,
flowcharting/data modeling, groupware, simulation software, and test data.
Interpretation of response items comprising the five factors suggested the following
constructs: ecommerce and advanced technologies, office automation, audit automation,
databases, and accounting firm office automation.
Tests of Hypotheses 1 and 2
In order to examine the first two hypotheses, a benchmark is needed against
which to measure the reported knowledge levels for the set of IT skills. We chose the
mid-point of the response range as the benchmark. Using a 7-point scale, the mid-point
is 3.5. For accounting educators, 25 of 36 IT skills are below the mid-point, and for
accounting practitioners, 27 of 36 IT skills are below the mid-point. Even if we drop the
13
benchmark to 3.0, for accounting educators, 19 of 36 IT skills are still below it, and for
accounting practitioners, 25 of 36 are still below it.
Because 53% of the IT skills yield a mean score of 3.0 or below for the
educators, this provides evidence that H1 should be rejected. Further, since 69% of the
IT skills yield a mean score of 3.0 or below for the practitioners, this provides evidence
that H2 should also be rejected. Thus, the perceived knowledge level of the 36 critical
IT skills is on the low side of the scale and indicates that both educators and
practitioners may not have the most relevant and current IT skill sets.
We also tested H1 and H2 using a Z test of proportions to compare mean
knowledge levels for academics and practitioners. We compared the proportion of IT
skills with mean knowledge levels equal to or below 3 with those equal to or above 4.
Mean knowledge levels from 3 to 4 were excluded from the test since we wanted to
compare “less knowledgeable” and “more knowledgeable” levels of knowledge. The null
hypothesis was that the proportion in the segment equal to or below 3 would be no
greater than 50% of the population. The alternate hypothesis was that a statistically
significant higher proportion of the population would be in the lower segment.
HO π ≤ .50
HA π › .50
The critical Z for a 1 tailed test at a .05 level of significance is 1.64. The computed Z
was 2.78 for academics and 7.16 for practitioners. Thus, the null hypothesis was
rejected for both groups and we can conclude that a statistically significant number of
respondents in both groups rated their knowledge levels low.
The above analysis, however, is in aggregate for all 36 technologies, so we
disaggregated them for the five components found in the factor analysis in order to
determine if academicians and practitioners were more or less knowledgeable on the 5
constructs. The following results are found for the educators:
14
□
Ecommerce & advanced technologies – H1 reject – 100% below the midpoint
□
Office Automation – H1 accept – 100% above the mid-point
□
Audit Automation – H1 reject – 100% below mid-point
□
Database – H1 – accept – 100% above mid-point
□
Accounting Firm Office Automation – accept 75% above mid-point
For the accounting practitioners, the following results are found :
□
Ecommerce & advanced technologies – H1 reject – 100% below the midpoint
□
Office Automation – H1 accept – 83% above the mid-point
□
Audit Automation – H1 reject – 75% below mid-point
□
Database – H1 – unclear – 50% below mid-point
□
Accounting Firm Office Automation – accept 100% above mid-point
Regarding the educators, we further examined the data to determine whether the IS,
audit and e-commerce educators have similar reported skill levels. Using the MannWhitney test of ranks, we find the following results to be significant at =.05 or better:

Professors teaching e-commerce reported higher skill levels than those not
teaching e-commerce for 31 of the 36 IT skills

Professors teaching “AIS only” reported higher skill levels than those teaching
“Audit only” for 33 of the 36 IT skills

Professors teaching “AIS and Audit” reported higher skill levels than those
teaching “Audit Only” for 35 of the 36 IT skills
A t-test reveals that professors that teach “Audit only” have significantly (=.001) more
teaching experience (15 years) than those teaching “AIS only” (9.6 years), yet a t-test of
age shows no statistical difference in age (48.4 vs. 49.5 mean years in age). This is
interesting as audit professors have more experience teaching, but report lower IT
15
knowledge levels. A possible explanation is that professors teaching “AIS only” had
more professional experience before entering academia.
Regarding the practitioner respondents, comparisons are made on responses
between those individuals working for firms that perform traditional audits and those that
do not. Significant differences are found for 5 of the 36 IT skills levels. For the following
4 skills, individuals working for traditional audit firms report significantly higher scores at
=.05:

Electronic working papers

Generalized audit software

Time management & billing systems

Tax return preparation software
Respondents from non-audit accounting firms report significantly higher scores on
one item: workflow technology. A final comparison was made of the 33 practitioner
respondents who indicated that their firms performed audits and their practice work area
was primarily auditing against the remainder of the 246 practitioner respondents
representing all other areas of accounting practice. No statistically significant
differences were found for any of the 36 information technology knowledge areas.
Test of Hypothesis 3
In the previous section, we found that the skill sets of both educators and
practitioners were on the low side of a 7-point scale. In this section, the relative skill sets
of the two groups are examined. Using the Mann-Whitney test of ranks, we find
significant differences for 30 of the 36 IT skills. Specifically, we find that academicians
report statistically higher levels (=.05 or better) than practitioners for 26 of the 36 (72%)
IT skills as listed in Table 9. For four of the items, practitioners score statistically higher
(=.05 or better) than the academicians. The four items on which the practitioners score
16
themselves higher than do their academic counterparts are: electronic working papers,
time management and billing systems, small business accounting software, and tax
return preparation software. The latter three items all loaded onto one construct in the
factor analysis: accounting firm office automation, and is not surprising that the
practitioners reported higher skill levels for this category.
Since the skill levels differ significantly for 83% of the items, we believe this
provides sufficient evidence to reject H3 for four of the constructs: e-commerce and
advanced technologies, office automation, audit automation, and databases. Except for
the accounting firm office automation construct, the educators tend to have higher
reported skill levels than the practitioners.
Test of Hypothesis 4, 5, and 6
In order to examine hypotheses four and five regarding whether IT skills should
be initially learned before entering practice, the second part of the questionnaire asked
respondents to indicate “Where is the single best place to initially learn each of the
technologies listed below?” The respondents were then provided a list of the same 36
technologies and asked to indicate one of three choices. These choices were 1. Before
College or University, 2. College or University, and 3. Accounting/Auditing Practice [they
could also choose “No Opinion”]. The frequencies of responses are reported in Table
10 for both educators and practitioners. In order to conduct meaningful tests of
distributions between before or during college and during accounting practice, the “no
opinion” responses were not included in the relative frequency calculations.
Chi-
squared tests of proportions comparing the relative frequencies of the “before college”
combined with “college or university” cells are compared with the frequency of
responses in the “accounting practice” cells. Thus, this test examines whether the
respondents are more likely to favor having the material initially covered either before or
during college (pre-practice) or during practice.
17
For educators, the results indicate (=.05 or better) that for 19 of the 36
technologies, they favor covering the material either before or during college as opposed
to during practice, thus we find no strong evidence to reject H4. However, an
examination of the remaining 17 technologies provides some interesting insight into
curriculum issues. For 6 of the 36 technologies, the educators favor covering the
material initially during practice, which generally means after college. The six
technologies that educators reported as more likely to favor being initially covered in
practice are:

Electronic working papers

Embedded audit modules

Real-time audit modules

Time management and billing systems

Agent technologies

Application service providers
For 11 of the 36 technologies, educators were equally likely to prefer initial coverage
before or during practice:

Generalized office software

Expert systems

Encryption software

Workflow technology

Test data

Firewall software/hardware

User authentication systems

Wireless communications

Intrusion detection & monitoring

Internal network configurations

External network configurations
18
For practitioners, the results indicate (=.05 or better) that for 23 of the 36
technologies [64%], they favor covering the material either before or during college as
opposed to during practice, thus we accept H5. Once again, examining the remaining 13
technologies provides some interesting insight into curriculum issues. For 6 of the 36
technologies, the practitioners favor covering the material initially during practice. The six
technologies that practitioners reported as more likely to favor being initially covered in
practice are:

Expert systems

*Embedded audit modules

*Real-time audit modules

*Time management & billing systems

Tax return preparation software

*Agent technologies
The items with an asterisk indicate they were also determined by the educators to
best be covered initially in practice. For 7 of the 36 technologies, practitioners were
equally likely to prefer initial coverage before or during practice:

electronic working papers

*generalized office software

*workflow technology

*test data

small business accounting software

enterprise resource planning

application service providers
In order to test H6, whether educators and practitioners agree on the best place to
initially learn these skills, a Kolmogorov-Smirnov test of distributions was conducted to
see whether differences exist between the educators and practitioners. Significant
differences (=.05 or better) exist for 9 of the 36 technologies as indicated in Table 10.
19
Since differences only exist for 25% of the IT skills, H6 is not rejected. However, the
differences found on specific items raise some interesting curriculum issues. A synopsis
of the findings and their curriculum implications are listed (“before practice” is used to
refer to “before or during college”):

Practitioners indicate that expert systems, encryption software, wireless
communications, intrusion detection and monitoring1, and external network
configurations should be initially covered before practice, while educators were split
between before or during practice. This can be categorized as a potential learning
gap. Practitioners indicate the importance of learning such skills prior to entering
practice, hence, educators need to seriously consider incorporating the coverage of
such items into the curriculum.

Educators indicate that electronic working papers should be initially covered during
practice, while the practitioners were split between before or during practice.
Students may benefit from learning such skills in college, but the opportunity cost of
time spent learning this skill as opposed to other technologies should be considered.

Educators indicate that small business accounting software and ERPs should be
initially covered before practice, while practitioners were split between before or
during practice. Depending on the career goals of the students, learning such
software packages/systems may be beneficial, but may not be valued by all
employers.

Both educators and practitioners believed that CASE tools should be initially covered
before practice, however, proportionally more educators indicated this preference.
The issue is not whether to cover this topic, but at what detail it should be covered.

Educators indicate that tax return preparation software should be initially covered
before practice, while the practitioners indicate that it should be covered during
practice. The implication is that this can be taught during practice and is probably
not valued by recruiters. However, a different response may occur in a study of tax
practitioners. However, 8 of the 12 tax practitioners in this study also indicate that
tax preparation software should initially be learned in practice.
The first item on the list indicates a potential learning gap if the practitioners count on
the educators to transfer knowledge of these skills to the students and the educators are
20
not entirely convinced of the need for coverage before students matriculate. Findings
regarding expert systems coverage are consistent with results by Bryant, Weishar, and
Fordham (1999) where they find that 50% of AIS programs include this topic in a course;
however, only 13% required the course. They also find that 63% of AIS programs
include a telecommunications course, including networking, but only 25% require such a
course. Also, their study does not include an analysis of the details of networking
coverage. They also report that these courses are domained outside of accounting, and
they raise the question whether the relevance of such topics to AIS get covered in such
an environment.
Additional Exploratory Analysis
The five technologies about which educators would most like to know more about
are listed below in descending order:

Enterprise resource planning

Generalized audit software

Electronic data interchange-web based

Embedded audit modules

Database design & installation
Interestingly, none of the technologies identified as falling into the “potential learning
gap” category make this list, although the educators’ mean scores on these items are
quite low (7-point scale): expert systems, 3.03; encryption software, 2.21; wireless
communications, 2.61; intrusion detection and monitoring, 2.12; and external network
configurations, 2.28. In fact, 3 of these technologies are among the five lowest scores,
yet they do not make the list of top 5 technologies for desired further learning by
educators. This indicates, perhaps, a sluggishness among educators to learn some of
the newer technologies.
1
Intrusion detection & Monitoring was marginally significant (=.067) for academicians, but to be
consistent =.05 is used.
21
The five technologies that practitioners would most like to know more about are
listed below in descending order based on the number of practitioners selecting the
technology:

EDI-Web-based

Electronic Presentations

Database Design & Installation

Simulation software

Internet Search & Retrieval
Regarding educators’ and practitioners’ opinions regarding the best mix of theory
and practical applications for teaching IT in a college or university, approximately onehalf of each group felt that it should be equally split between theory and practice. Figure
3 shows the distributions of responses for both practitioners and educators. A
Kolmogorov-Smirnov Test of distributions reveals a slightly significant difference in the
distributions (=.075). From this test and an examination of the frequency of responses,
we conclude that, for the most part, educators and practitioners agree on a 50-50 split
between theory and practical applications, with practitioners having a slight leaning
towards a little more practical applications and educators leaning slightly towards more
theoretical pedagogy.
For educators, a one-way analysis of variance2 with gender as the factor
[independent variable] found that knowledge on 6 of 36 information technologies was
significantly different at = .05, and that the male respondents reported higher
knowledge levels for each of the 6 IT skills:
2

Electronic working papers

Generalized audit software

Embedded audit modules

Simulation software
These results are also consistent with Mann-Whitney tests.
22

Flowcharting/data modeling

User authentication systems
For the practitioners, the differences found in gender were much more
pronounced. Males scored statistically higher (= .05) on 20 out of 36 technologies.
Next, an analysis was conducted to examine whether gender was still significant, after
considering experience level, by running the following regression equation:
Kn = Intercept + Gender + Experience + (Gender*Experience)
Where Kn = each of the 36 IT knowledge levele
Gender = 1 female; 2 male
Experience = Years experience reported
The significance of the gender variables are reported in Table 11. For practitioners,
gender was significant for 16 of the 36 technologies after considering their experience in
years. For educators, only two significant differences were found after considering
experience levels. Thus, gender appears to be a significant factor in reported
knowledge levels for practitioners, but not for academicians. Due to the design of the
questionnaire, determining whether actual knowledge by males of the technologies was
in fact higher or whether response differences were due to factors such as selfconfidence is not possible to discern. The literature on gender differences in self-ratings
is very mixed. Atwater and Roush (1994) and Snyder and Bruning (1979) find no
significant differences in self-ratings between male and females, yet other researchers,
such as Deaux (1979) find that male self-evaluations tend to be higher than their female
counterparts. Gould & Slone (1982) assert that lower self-ratings by females stems from
their desire to appear modest, and thus the self-ratings are not necessarily indicative of
their actual skill level. Further research in this area to examine actual skill levels of
female vs. male accountants, as well as the professional development programs
assigned to them within their firms is an avenue of further research.
23
LIMITATIONS
As is common with all types of research, this study has a number of possible
limitations that may limit the validity of any results. An understanding of these limitations
will facilitate making correct interpretations of the results of this study. The limitations
deemed most significant are discussed below.
One possible limitation of survey research is a non-response bias. This occurs
when the knowledge and beliefs of persons not responding to the questionnaire are
significantly different from those individuals who responded. We have no data to
compare respondents with non-respondents, but we can compare early vs. late
respondents. An ANOVA test is conducted comparing Early (first 60% of respondents)
versus Late respondents (last 40% of respondents) for each of the 36 IT knowledge
levels and educational placement items on the questionnaire. For educators
(practitioners), only seven (nine) of the 72 survey items had response differences that
were statistically significant at the .05 level using the F statistic. Four (eight) of these
items involved knowledge of the thirty-six technologies and three (one) involved
educational location for learning the thirty-six technologies. The early respondents
reported higher skill levels than the later respondents. This result may be interpreted to
mean that individuals who are more interested in technology tend to respond at a higher
rate and display a higher rate of knowledge about technology. We believe the nonresponse bias for this survey to be mild and not to significantly affect the results of the
survey.
The thirty-six technologies examined in this research were subjectively selected
by the researchers based on a detailed literature review.
Some equally significant
technologies may have been inadvertently overlooked in the literature and, thus, are not
included in this research nor considered in the discussion of implications.
24
Respondents in this study self-reported views about their own information
technology knowledge. They may, however, hold incorrect views about their knowledge
of these technologies and their actual knowledge may be higher or lower than that which
was reported. Economic considerations did not permit an actual test of respondents’
technology knowledge. Because the number of female respondents for the practitioners
was proportionally higher than the 50-50 sample, the results may be influenced by their
lower self-ratings than males for 20 of the 36 technologies. As mentioned earlier, further
research into the relative accuracy rates of males vs. females on their self-rating of IT
skills is necessary.
CONCLUSIONS AND IMPLICATIONS
Our findings for the six hypotheses proposed were:

H1: The perceived knowledge of AIS and auditing educators includes
relevant, current information technologies. REJECTED for two
constructs: e-commerce and advanced technologies and audit
automation, but ACCEPTED for three constructs: office automation,
databases, and accounting firm office automation.

H2: The perceived knowledge of auditing practitioners includes relevant,
current information technologies. REJECTED for two constructs: ecommerce and advanced technologies, and ACCEPTED for two
constructs: office automation, and accounting firm office
automation. The results were INCONCLUSIVE for the database
construct.

H3: AIS and auditing educators have the same perceived knowledge of
relevant, current information technologies as audit practitioners.
25
REJECTED since 83% of IT knowledge levels were statistically
significantly different.

H4: AIS and auditing educators believe that relevant, current information
technologies should be initially learned before entering practice.
INCONCLUSIVE as, at a statistically significant level, only 53% of IT
technologies were preferred to be learned before practice.

H5: Audit practitioners believe that relevant, current information
technologies should be initially learned before entering practice.
ACCEPTED as, at a statistically significant level, 64% of IT
technologies were preferred to be learned before practice.

H6: AIS and auditing educators agree on the best place to initially learn
relevant, current information technologies. ACCEPTED as, at a
statistically significant level, differences existed for only 25% of the
IT technologies.
Successful university coverage of auditing/accounting information systems
requires that the educators teaching in this area acquire and maintain competence in IT.
The survey reveals that the IT knowledge of educators and practitioners varies widely.
For the educators, significant differences exist within the academic community between
those individuals that teach e-commerce, AIS, and audit. A large number of both
educators and professionals indicate either no knowledge or extremely low knowledge of
some of today’s critical business technologies, such as wireless communications,
software security tools, network configurations, and workflow technology. The
implications are serious for successful accounting education. The potential “learning
gap” identified five technologies valued by practitioners that are in danger of not
adequately being covered in college by knowledgeable educators: expert systems,
26
encryption software, wireless communications, intrusion detection and monitoring and
external network configurations.
One segment of the survey allowed respondents to identify the technologies they
would most like to know more about. Their responses could be interpreted as
information about the perceived relevance of these technologies for education.
Interestingly, none of the IT skills for which educators or practitioners scored the lowest
made it to these lists. This may indicate that so little is known about these IT skills that
members of the academic and practitioner communities do not even yet understand their
relevance.
An important objective of this study is to initiate discussion, debate, and action
that will lead to positive changes in the auditing/AIS education as it moves forward in
today’s techno-intensified environment. We hope that these survey results will help both
initiate faculty and practitioner development and focus the debate on necessary IT skills.
27
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29
Figure 1
Hypotheses Tested
36 Identified Information
Technologies that are important to the
Assurance Profession
Survey of
AIS/
Assurance
Educators
Assessment of
best place to
learn
the 36 ITs
Survey of
AIS/
Assurance
Practitioners
H 6:
AIS/audit educators
and practitioners
agree on the best
place to learn IT skills
H4:
AIS/audit educators
believe that relevant, current
information technologies should
be initially learned before entering
practice
Measure of
knowledge
level of
the 36 ITs
H1 The perceived
knowledge of AIS/
auditing educators includes
relevant, current
information technologies
Assessment of
best place to
learn
the 36 ITs
H 5:
Audit practitioners
believe that relevant, current
information technologies should
be initially learned before entering
practice
H 3:
AIS/auditing educators have the
same perceived knowledge of
relevant, current
information technologies as
audit practitioners
Measure of
knowledge
level of
the 36 ITs
H2 The perceived
knowledge of auditing
practitioners includes
relevant, current
information technologies
30
Table 1
Information Technology Definitions
ITEM INFORMATION TECHNOLOGY Source
IFAC 11
computer program that facilitates entry and preparation of
1 Word Processing
documents such as letters or reports.
2
Electronic Spreadsheets
IFAC 11
software which allows the auditor to enter either
alphanumeric or numeric data and manipulate it either via
standard functions or auditor programmed functions
3
E-Mail
IFAC 11
4
Electronic Working Papers
IFAC 11
exchange of mail messages via Intranets and/or the
Internet.
software which generates a trial balance, lead schedules,
and other schedules useful for the recording of evidence in
an audit or assurance engagement
5
Internet Search & Retrieval
AICPA ‘94 permits user to search text that is in electronic format and
retrieve, view, and print desired text.
6
Image Processing
Helms &
conversion of paper documents into electronic form through
Mancino ‘97 scanning and the subsequent storage and retrieval of the
electronic image
7
Electronic Presentations
IFAC 11
8
Generalized Audit Software
IFAC 11
9
Expert Systems
IFAC 11
software that facilitates the organization and use of text,
voice, and/or images to communicate concepts
computer program which helps the auditor access client
computer data files, extract relevant data, and perform
some audit function such as addition or comparison.
computer software that provides relevant information and/or
decision models to assist a human in making a decision or
accomplishing some task.
10 Embedded Audit Modules
11 Real-time Audit Modules
12 Database Search & Retrieval
AICPA ‘94
IFAC 11
software that uses relational structures between data files
and facilitates varying data retrieval and use.
13 Simulation Software
Elliott ‘94
abstraction of some aspect of real system via software.
Auditor may use model to evaluate the reliability of
information from real world sources. This may be thought of
as a very high level analytical review of a company’s data.
14 Flowcharting/Data Modeling
AICPA ‘94 software using the source code version of programs to
produce flowcharts of program logic
15 Computer Aided Systems Engineering IFAC 11
Tools
programmed routines incorporated into an application
program which are designed to perform an audit function
integrated package of computer tools that automate
important aspects of the software development process to
increase software development effectiveness in terms of
productivity of systems development and quality of
developed systems.
16 Encryption Software
Helms &
changing data using some type of encoding/decoding
Mancino ‘97 algorithm so that unauthorized persons who can access the
encrypted data will not be able to read it or use it.
17 Groupware
Glover &
software that permits auditors to categorize, store, and share
Romney ‘97 data among themselves as well as communicate with each
other about that data, preferably in a real-time mode.
distribution of processing functions between two or
Mancino ‘97 more computers as in a local area network. This also
includes end-user computing where users on the
network also process and store data on their personal
computers.
18 Cooperative Client/Server Environment Helms &
19 Workflow Technology
AICPA Top software and hardware that facilitates the capture of data in
10 ‘97
the work place to improve management of the business. For
example, using an electronic scanner to record the
movement of materials in a warehouse based on the
barcodes on the materials.
20 Database Design & Installation
IFAC 11
software that permits the creation and use of relational
structures between data files
21 Time Management & Billing Systems
IFAC 11
computer program which assists in capturing, managing,
billing, and reporting time spent on professional activities.
22 Test Data
IFAC 11
a set of transactions processed by the auditor to test the
programmed or procedural operations of a computer
application
23 Small Business Accounting Software
IFAC 11
accounting software package used to record transactions,
maintain general and subsidiary ledgers, and generate
financial statements.
24 Digital Communications
AICPA Top bandwidth – telecommunications devices used to facilitate
10 2000
the rapid and unfettered transfer of data.
IFAC 11
software, perhaps incorporating expert knowledge, which
assists the accountant/auditor in identifying relevant
information, capturing and recording it in a manner that can
be filed with tax authorities.
25 Tax Return Preparation Software
AICPA Top
10 2000
AICPA Top
10 2000
Part of “security technology” that enforces an access control
policy between two networks.
devices used to verify that a system user is who he/she
claims to be.
28 EDI-Traditional
IFAC 11
transfer of data or payments electronically between
computers using software that may, or may not, require
human intervention to affect the transfer.
29 EDI-Web Based
Greenstein The extension to XML-based EDI
& Feinman,
2000
AICPA Top the ability to transfer digital data without the use of cables,
10 2000
twisted-pair, or fiber optics.
26 Firewall Software/Hardware
27 User Authentication Systems
30 Wireless Communications
31 Agent Technologies
AICPA Top programmed modules that are given certain levels of
10 2000
authority and autonomy to act on behalf of their “supervisor”,
such as to decide whether to order more inventory and from
which supplier
32 Intrusion Detection & Monitoring
AICPA Top Part of “security technology” that identifies unauthorized
10 2000 & requests for services
Greenstein
& Feinman
IFAC 11
linkage of individuals and data through hardware and
software systems that permit the exchange of various types
of data.
33 Internal Network Configurations
34 External Network Configurations
AICPA Top – intranet, extranet, and Internet access devices than enable
10 2000
users physically separated from the server to access it.
35 Enterprise Resource Planning
McKee
2000
McKee
2000
36 Application Service Providers
03/08/16
business-wide information systems that cross boundaries
Companies which host (provide hardware, software and
connectivity) for specific business applications
32
Table 2
Sample and Response Rate
Academicians
Survey Sample Size
Gross Responses
1,000
328
Gross Response Rate
Adjusted Sample Size:
Did not complete because:
Do not teach AIS or audit
Retired
On leave of absence
Were researchers conducting this
study
Did not know the answers
Adjusted Sample Size
Completed Surveys
Adjusted Response Rate
% of respondents from 1st mailing
% of respondents from 2nd mailing
03/08/16
Practitioners
1,000
249
32.8%
1,000
-
24.9%
1,000
23
1
1
2
973
3
997
304
31.2%
246
24.7%
68.4%
31.6%
59.8%
40.2%
33
Table 3
Demographic Data
N
Academicians
Practitioners
304
246
25.0%
73.4%
1.6%
59.4%
39.0%
1.6%
48.7
32
76
8.6
42.2
23
72
9.9
12.3
0
37
8.6
16.6
0
51
8.7
Gender
Female
Male
Missing Data
Age
Average
Minimum
Maximum
Standard Deviation
Experience
Average
Minimum
Maximum
Standard Deviation
Teaching
Subjects/Position
IS Only
Audit Only
E-commerce Only
IS and Audit
IS and Ecommerce
Audit and Ecommerce
IS, Audit & Ecommerce
None of the 3
subjects
Missing Data
Highest Degree
03/08/16
68 Firm Performs
Audit
130 Firm Does not
Perform Audit
3 Missing Data
53
13
2
103
141
3
8
23
4
UG
68.7%
Grad
29.3%
Ph.D.
.01%
34
Table 3-Continued
Demographic Data For Respondents Working For Firms That Perform
Traditional Audits
Current Position
Practice Area
Staff
6
Audit
33
Senior supervisor
16 Government & Non Profit
6
Manager
24
Tax
12
Partner
52
Financial Services
5
Other
4
General
23
Missing
1
Other
5
Missing
19
Total
103
Total
103
03/08/16
35
Table 4
Significant Correlations for Academicians
=.05 or better
Age is significantly negatively correlated with (Spearman correlation coefficient)
perceived knowledge on:
Word processing
Electronic spreadsheets
Internet search & retrieval
Cooperative Client/Server Environment
Gender (female=1, male=2) is significantly positively correlated with (Spearman
correlation coefficient) perceived knowledge on:
Electronic working papers
Generalized Audit Software
Simulation Software
Flowcharting/Data Modeling
User Authentication Systems
Years of Experience Teaching AIS/Audit is significantly positively correlated with
(Spearman correlation coefficient) perceived knowledge on:
Positive correlation:
Electronic Working Papers
Generalized Audit Software
Embedded Audit Modules
Real-time Audit Modules
Flowcharting/Data Modeling
Test Data
Firewall Hardware/Software
User Authentication Systems
EDI-Traditional
EDI-Web-based
Intrusion Detection
Application Service Providers
Negative correlation:
Word Processing
Electronic Spreadsheets
Age and Gender were also statistically significantly positively correlated.
03/08/16
36
Table 5
Significant Correlations for Practitioners =.05 or better
Age is significantly negatively correlated with (Spearman correlation coefficient)
perceived knowledge on:
Word processing
Electronic spreadsheets
E-mail
Groupware
Gender (female=1, male=2) is significantly positively correlated with (Spearman
correlation coefficient) perceived knowledge on:
Internet search retrieval
Generalized audit software
Expert Systems
Computer Aided Systems Engineering Tools
Encryption Software
Groupware
Cooperative Client/Server Environment
Workflow Technology
Time Management & Billing Systems
Test Data
Digital Communications
Tax Return Preparation Software
Firewall Software/Hardware
User Authentication Systems
EDI-Traditional
EDI-Web Based
Wireless Communications
Agent Technologies
Intrusion Detection & Monitoring
Internal Network Configurations
External Network Configurations
Enterprise Resource Planning
Application Service Providers
Years of Experience Teaching AIS/Audit is significantly positively or negatively
correlated with (Spearman correlation coefficient) perceived knowledge on:
Positive correlation:
Database Design & Installation
Time Management & Billing
Small Business Accounting Software
EDI-Traditional
Agent Technologies
Application Service Providers
Negative correlation:
Groupware
Age and Gender are also statistically significantly positively correlated.
Experience and Gender are also statistically significantly positively correlated.
03/08/16
37
Table 6
Information Technology Knowledge
Academicians
The first part of the questionnaire asked respondents to rank their own knowledge of
36 information technologies on a 1 to 7 scale where 1 = No Knowledge and 7 = Expert
Knowledge. The responses are sorted into descending order based on the means.
RANK
ORDER
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
INFORMATION TECHNOLOGY
Word Processing
E-Mail
Electronic Spreadsheets
Internet Search & Retrieval
Electronic Presentations
Small Business Accounting Software
Database Search & Retrieval
Tax Return Preparation Software
Flowcharting/Data Modeling
Image Processing
Database Design & Installation
Electronic Working Papers
Simulation Software
Test Data
Generalized Audit Software
EDI-Traditional
Expert Systems
EDI-Web Based
Enterprise Resource Planning
Groupware
Time Management & Billing Systems
Cooperative Client/Server Environment
Wireless Communications
Workflow Technology
Embedded Audit Modules
Application Service Providers
Real-time Audit Modules
Digital Communications
User Authentication Systems
Firewall Software/Hardware
Internal Network Configurations
External Network Configurations
Computer Aided Systems Engineering Tools
Encryption Software
Intrusion Detection & Monitoring
Agent Technologies
03/08/16
MEAN
5.66
5.57
5.29
5.21
4.94
4.16
4.11
3.97
3.80
3.71
3.48
3.20
3.19
3.19
3.11
3.07
3.03
2.97
2.86
2.85
2.79
2.71
2.61
2.60
2.59
2.58
2.57
2.51
2.38
2.37
2.31
2.28
2.22
2.21
2.12
2.05
STANDARD
DEVIATION
0.96
0.98
1.24
1.23
1.55
1.65
1.67
1.75
1.81
1.69
1.88
1.66
1.65
1.78
1.64
1.65
1.59
1.65
1.77
1.66
1.59
1.63
1.50
1.55
1.54
1.63
1.51
1.67
1.56
1.46
1.56
1.49
1.50
1.48
1.41
1.37
38
Table 7
Information Technology Knowledge
Practitioners
The first part of the questionnaire asked respondents to rank their own knowledge of
36 information technologies on a 1 to 7 scale where 1 = No Knowledge and 7 = Expert
Knowledge. The responses are sorted into descending order based on the means.
RANK
ORDER
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
INFORMATION TECHNOLOGY
Electronic Spreadsheets
E-Mail
Word Processing
Tax Return Preparation Software
Internet Search & Retrieval
Small Business Accounting Software
Electronic Working Papers
Time Management & Billing Systems
Database Search & Retrieval
Electronic Presentations
Flowcharting/Data Modeling
Generalized Audit Software
Image Processing
Groupware
Wireless Communications
Cooperative Client/Server Environment
Test Data
Simulation Software
Database Design & Installation
EDI-Traditional
EDI-Web Based
Real-time Audit Modules
Internal Network Configurations
Embedded Audit Modules
Expert Systems
Application Service Providers
Workflow Technology
Digital Communications
Firewall Software/Hardware
Enterprise Resource Planning
Agent Technologies
External Network Configurations
User Authentication Systems
Intrusion Detection & Monitoring
Computer Aided Systems Engineering Tools
Encryption Software
03/08/16
MEAN
5.38
5.09
4.99
4.83
4.78
4.52
3.70
3.67
3.51
3.38
2.94
2.94
2.86
2.80
2.78
2.73
2.67
2.64
2.48
2.24
2.20
2.10
2.11
2.08
2.00
1.98
1.96
1.89
1.85
1.85
1.81
1.78
1.77
1.67
1.66
1.59
STANDARD
DEVIATION
1.20
1.17
1.18
1.84
1.32
1.72
1.73
1.77
1.59
1.77
1.60
1.68
1.60
1.71
1.63
1.65
1.69
1.54
1.52
1.47
1.42
1.42
1.44
1.42
1.35
1.40
1.28
1.28
1.25
1.42
1.30
1.23
1.27
1.19
1.08
1.06
39
Table 8
Factor Analysis of 36 Technology Levels
E-commerce & Advanced
Technologies
31%
Office
Automation
42%
Audit Automation
52%
Database
60%
Accounting Firm
Office Automation
68%
CASE Tools
Encryption Software
Client/Server Env.
Workflow Technology
Digital Comm.
Firewalls
User Authentication
EDI-Traditional
EDI-Web Based
Wireless Comm.
Agent Technologies
Intrusion Detection &
Monitoring
Internal Network Config.
External Network Config.
ERP
ASPs
Word Processing
E-Spreadsheets
E-Mail
Internet Search
& Retrieval
Image
Processing
E-Presentations
E-Working
Papers
Generalized
Audit Software
Embedded Audit
Modules
Real-time Audit
Modules
Database
Search &
Retrieval
Database
Design &
Installation
Time
Management &
Billing Systems
Small Business
Accounting
Software
Tax Return
Preparation
Software
Varimax Factor Analysis
03/08/16
40
Table 9
Information Technology Knowledge Comparison
Academicians vs. Practitioners
ITEM
NUMBER
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
INFORMATION TECHNOLOGY
Word Processing
Electronic Spreadsheets
E-Mail
Electronic Working Papers
Internet Search & Retrieval
Image Processing
Electronic Presentations
Generalized Audit Software
Expert Systems
Embedded Audit Modules
Real-time Audit Modules
Database Search & Retrieval
Simulation Software
Flowcharting/Data Modeling
Computer Aided Systems Engineering
Tools
Encryption Software
Groupware
Cooperative Client/Server Environment
Workflow Technology
Database Design & Installation
Time Management & Billing Systems
Test Data
Small Business Accounting Software
Digital Communications
Tax Return Preparation Software
Firewall Software/Hardware
User Authentication Systems
EDI-Traditional
EDI-Web Based
Wireless Communications
Agent Technologies
Intrusion Detection & Monitoring
Internal Network Configurations
External Network Configurations
Enterprise Resource Planning
Application Service Providers
Mann-Whitney
Z-value
Significance
(2-tailed)
-6.80
-.89
-4.76
-3.44
-3.75
-5.78
-9.80
-1.19
-8.30
-4.50
-4.15
-4.12
-4.07
-5.54
.000*
.373
.000*
.001*
.000*
.000*
.000*
.233
.000*
.000*
.000*
.000*
.000*
.000*
A
-4.64
-5.60
-.52
-.069
-5.01
-6.23
-5.77
-3.56
-2.75
-4.50
-5.73
-4.58
-5.45
-6.24
-5.63
-1.01
-2.57
-4.46
-1.57
-4.50
-7.42
-4.70
.000*
.000*
.602
.945
.000*
.000*
.000*
.000*
.006*
.000*
.000*
.000*
.000*
.000*
.000*
.315
.010*
.000*
.116
.000*
.000*
.000*
A
A
A
P
A
A
A
A
A
A
A
A
A
A
A
P
A
P
A
P
A
A
A
A
A
A
A
A
A
* significant at .01 or better
A Academician has higher statistically higher mean
P Practitioner has higher statistically higher mean
03/08/16
41
Table 10
Education Location Comparison – C=Before or during College; P=During Practice
Academicians vs. Practitioners
Before
College or
University
College or
University
Accounting/
Audit
Practice
2
significance
K-S
significance
1. Word Processing
Software
Academicians
92.3%
7.7%
0%
.000* C .38
Practitioners
80.9%
13.3%
5.8%
.000* C
2. Electronic
Spreadsheets
Academicians
54.9%
45.1%
0%
.000* C .34
Practitioners
58.8%
35.0%
6.2%
.000* C
3. E-Mail
Academicians
89.3%
10.4%
.3%
.000* C .09
Practitioners
79.1%
11.1%
9.8%
.000* C
4. Electronic Working
Papers
Academicians
2.8%
39.4%
57.8%
.009* P .01*
Practitioners
6.1%
49.1%
44.8%
.112
5. Internet Search &
Retrieval
Academicians
75.6%
24.1%
.3%
.000* C .40
Practitioners
78.5%
15.5%
6.0%
.000* C
6. Image Processing
Academicians
54.5%
34.9%
10.6%
.000* C .47
Practitioners
52.3%
32.2%
15.5%
.000* C
7. Electronic
Presentations
Academicians
32.4%
64.8%
2.8%
.000* C .11
Practitioners
30.2%
57.7%
12.1%
.000* C
8. Generalized Audit
Software
Academicians
.3%
50.2%
49.5%
.859
.44
Practitioners
1.8%
43.5%
54.7%
.16
9. Expert Systems
Academicians
.4%
49.8%
49.8%
.952
.00*
Practitioners
1.6%
23.5%
74.9%
.000* P
10. Embedded Audit
Modules
Academicians
.3%
33.5%
66.2%
.000* P .50
Practitioners
1.0%
32.7%
66.3%
.000* P
11. Real-time Audit
Modules
Academicians
.3%
29.5%
70.2%
.000* P .47
Practitioners
1.0%
33.8%
65.2%
.000* P
12. Database Search &
Retrieval
Academicians
6.9%
87.5%
5.6%
.000* C .19
Practitioners
20.9%
65.5%
13.6%
.000* C
2
 test of proportions of before and during college or university vs. during accounting/audit
practice.Kolmogorow-Smirnov test of difference in distributions between academicians and practitioner –
pre, during college vs. during accounting/audit practice.
03/08/16
42
Table 10 (continued)
Education Location Comparison - C=Before or during College; P=During Practice
Academicians vs. Practitioners
13. Simulation Software
Academicians
Practitioners
14. Flowcharting/Data
Modeling
Academicians
Practitioners
15. CASE Tools
Academicians
Practitioners
16. Encryption Software
Academicians
Practitioners
17. Groupware
Academicians
Practitioners
18. Cooperative
Client/Server
Environment
Academicians
Practitioners
19. Workflow Technology
Academicians
Practitioners
20. Database Design &
Installation
Academicians
Practitioners
21. Time Management &
Billing Systems
Academicians
Practitioners
22. Test Data
Academicians
Practitioners
23. Small Business
Accounting Software
Academicians
Practitioners
24. Digital
Communications
Academicians
Practitioners
Before
College or
University
College or
University
Acct/
Audit
Practice
2
1.1%
3.3%
86.0%
73.2%
12.9%
23.5%
.000* C
.000* C
.07
.7%
6.9%
85.1%
77.0%
14.2%
16.1%
.000* C
.000* C
.50
1.8%
5.0%
60.1%
72.1%
38.2%
22.9%
.000* C
.000* C
.013*
2.2%
5.4%
48.50%
64.2%
49.3%
30.4%
.842
.000* C
.002*
7.8%
16.1%
55.1%
50.8%
37.1%
33.1%
.000* C
.000* C
.49
.8%
3.9%
55.7%
58.4%
43.5%
37.7%
.045* C
.001* C
.44
3.0%
5.1%
46.6%
49.0%
50.4%
45.9%
.896
.299
.49
.7%
8.3%
86.7%
73.4%
12.6%
18.3%
.000* C
.000* C
.43
.4%
2.3%
17.2%
15.8%
82.4%
81.9%
.000* P
.000* P
.50
.8%
3.3%
53.1%
52.7%
46.1%
44.0%
.217
.105
.50
1.8%
4.1%
61.3%
39.8%
36.9%
56.1%
.000* C
.069
.000*
3.2%
4.3%
58.0%
57.3%
38.8%
38.4%
.001* C
.006* C
.5
significance
K-S
significance
2 test of proportions of before and during college or university vs. during accounting/audit practice.
Kolmogorow-Smirnov test of difference in distributions between academicians and practitioner – pre, during college
vs. during accounting/audit practice.
03/08/16
43
Table 10 (continued)
Education Location Comparison – C=Before or during College; P=During Practice
Academicians vs. Practitioners
Before
College or
University
College or
University
Acct/
Audit
Practice
2
significance
K-S
significance
25. Tax Return
Preparation Software
Academicians
2.5%
63.3%
34.2%
.000* C .000*
Practitioners
4.0%
32.5%
63.5%
.000* P
26. Firewall/Software
Hardware
Academicians
.9%
55.2%
43.9%
.065
.22
Practitioners
5.6%
59.7%
34.7%
.000* C
27. User Authentication
Systems
Academicians
.4%
51.8%
47.8%
.540
.12
Practitioners
4.6%
59.1%
36.3%
.002* C
28. EDI-Traditional
Academicians
1.1%
73.0%
25.9%
.000 *C .09
Practitioners
6.1%
56.8%
37.1%
.002* C
29. EDI-Web-based
Academicians
2.3%
74.5%
23.2%
.000* C .09
Practitioners
8.1%
57.4%
34.5%
.000* C
30. Wireless
Communications
Academicians
10.9%
43.2%
45.9%
.225
.003*
Practitioners
25.7%
46.7%
27.6%
.000* C
31. Agent Technologies
Academicians
.4%
30.9%
68.7%
.000* P .31
Practitioners
4.0%
36.0%
60.0%
.025* P
32. Intrusion Detection &
Monitoring
Academicians
.9%
42.9%
56.2%
.067
.02*
Practitioners
7.5%
52.2%
40.3%
.025* C
33. Internal Network
Configurations
Academicians
0%
54.8%
45.2%
.156
.25
Practitioners
4.5%
59.0%
36.5%
.001* C
34. External Network
Configurations
Academicians
.4%
52.3%
47.3%
.418
.02*
Practitioners
4.8%
62.8%
32.4%
.000* C
35. Enterprise Resource
Planning
Academicians
0%
66.9%
33.1%
.000* C .003*
Practitioners
3.7%
45.3%
51.0%
.798
36. Application Service
Providers
Academicians
0%
42.2%
57.8%
.016* P .5
Practitioners
3.5%
39.2%
57.3%
.079
2 test of proportions of before and during college or university vs. during accounting/audit
practice. Kolmogorow-Smirnov test of difference in distributions between academicians and practitioner – pre,
during college vs. during accounting/audit practice.
03/08/16
44
Table 11 Gender Effects While Also Considering Experience
IT Knowledge= Intercept + Gender + Experience + ( Gender * Experience )
For practitioners [academics] gender was significant at the .091 level [.848 level ] and
experience was significant at the .661 level [.313 level], both employing Wilks’ Lambda.
ITEM
NUMBER
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
INFORMATION TECHNOLOGY
Word Processing
Electronic Spreadsheets
E-Mail
Electronic Working Papers
Internet Search & Retrieval
Image Processing
Electronic Presentations
Generalized Audit Software
Expert Systems
Embedded Audit Modules
Real-time Audit Modules
Database Search & Retrieval
Simulation Software
Flowcharting/Data Modeling
Computer Aided Systems Engineering
Tools
Encryption Software
Groupware
Cooperative Client/Server Environment
Workflow Technology
Database Design & Installation
Time Management & Billing Systems
Test Data
Small Business Accounting Software
Digital Communications
Tax Return Preparation Software
Firewall Software/Hardware
User Authentication Systems
EDI-Traditional
EDI-Web Based
Wireless Communications
Agent Technologies
Intrusion Detection & Monitoring
Internal Network Configurations
External Network Configurations
Enterprise Resource Planning
Application Service Providers
03/08/16
PRACTITIONERS Sig.
At
Significance of
.05
Between Subjects level
Effects For
Gender Based on
F Statistic
.48
.32
.01
*
.12
.001
*
.11
.04
*
.08
.03
*
.19
.19
.06
.25
.14
ACADEMICS
.71
.01
.001
.04
.04
.21
.26
.16
.93
.00
.36
.03
.08
.27
.05
.06
.01
.19
.03
.01
.02
.03
.29
.41
.21
.66
.79
.26
.56
.16
.30
.39
.11
.89
.13
.56
.64
.88
.20
.81
.50
.56
.56
.24
*
*
*
*
*
*
*
*
*
*
*
*
Sig.
At
Significance of
.05
Between Subjects level
Effects For
Gender Based on
F Statistic
.32
.05
*
.77
.14
.65
.28
.54
.20
.18
.14
.22
.10
.05
*
.09
45