The Antecedents of Internal Auditors’ Adoption of Continuous Auditing
Technology: Exploring UTAUT in an Organizational Context
ABSTRACT
The concept of Continuous auditing originated over two decades ago. Yet despite its much touted benefits
its acceptance and use in practice has been slow. To gain insight into the state of affairs, we surveyed 234
internal auditors worldwide on the status of their continuous auditing. Using the Unified Theory of
Acceptance and Use of Technology (UTAUT) we explore the antecedents of internal auditors’ intentions
to adopt continuous auditing technology. Employing the Partial Least Squares method, we find strong
support for the model with an R2 of 44.3%. Specifically, we find that internal auditors’ perceptions of
effort expectancy and social influence are significant predictors of their intentions to adopt continuous
auditing. We also find that annual sales volume of the company and voluntariness of use significantly
moderate the relationship between performance expectancy and social influence respectively.
Additionally, we present key observations regarding the internal auditors’ perceptions of the current state
of continuous auditing.
Keywords: Continuous Auditing, Continuous Assurance, Internal Auditing, UTAUT, Technology
Adoption.
1. Introduction
Continuous auditing has been touted as offering many important benefits to organizations.
Among them are the minimization of accounting errors, more timely analysis and organizational
communication, and increased audit efficiency and effectiveness. Various research studies have explained
the benefits of continuous auditing (Vasarhelyi et al., 2004; Kuhn and Sutton, 2006), discussed technical
aspects of implementing continuous auditing technology (Kuhn and Sutton, 2010), explored actual
implementations in practice (Herman et al., 2006), and examined the psychological effects of continuous
auditing on managers (Hunton et al., 2008; Hunton et al. 2010)1. Yet while the concept of continuous
auditing, first introduced by Groomer and Murthy (1989) and Vasarhelyi and Halper (1991), is about two
decades old, the actual practice of continuous auditing has remained the exception rather than the rule
(Chan and Vasarhelyi, 2011). This puzzling lag in the adoption of continuous auditing is the primary
motivation for this study.
1
For a review of the literature see Brown et al., 2007.
1
Adoption of continuous auditing technology has so far been almost exclusively limited to the
internal audit function (Chan and Vasarhelyi, 2011). Since 2005 some of the top international accounting
firms have surveyed their clients’ Chief Audit Executives (CAEs) and other top internal audit officers to
gain an understanding of their continuous auditing practices (Grant Thornton, 2011; KPMG, 2010; PwC,
2006). The results of these surveys vary in terms of how extensively continuous auditing was being
adopted in practice. For example, one survey showed that of the surveying accounting firm’s clients, 13%
had a continuous auditing system that was fully operational and 37% had a system in place but not yet
fully developed, while another firm’s survey indicated comparable figures of 7% and 13%, respectively
(see Table 1). One thing that consistently came across in these surveys, however, was the uniform
apparent optimism of the survey respondents. Regardless of the level to which continuous auditing had
been adopted, the survey respondents expected a considerably higher level of adoption in two years’ time.
Yet, taking into account the timing of these earlier surveys and this study’s results, it appears that the
implementation of continuous auditing has actually advanced very slowly. This again, raises the question
of why there seems to be a lag in the adoption of continuous auditing.
----------------------------------------- (Insert Table 1 about here) -----------------------------------Based on this study’s survey results and the results of prior surveys conducted by those firms, we
characterize the current implementation of continuous auditing as more experimentation and pilot testing
than full-blown adoption. Given the potential benefits of continuous auditing, some of which were
previously mentioned, this is puzzling. We thus set out to gain insight into this state of affairs by
conducting an online survey of industry practitioners’ internal audit practices and analyzing their
responses through the lens of the Unified Theory of Acceptance and Use of Technology (UTAUT) from
the Information Systems discipline (Venkatesh et al., 2003). UTAUT provides a theoretical framework
upon which to assess the adoption of a particular type of technology. In this study, we find that the
UTAUT model explains a substantial amount of variance in the intentions to adopt continuous auditing.
Most importantly, we find that perceptions of effort expectancy and social influence are significant
predictors of internal auditors’ intentions to adopt, while performance expectancy and facilitating
conditions are not. Annual sales volume of the company and voluntariness of use significantly moderate
the relationship between performance expectancy and social influence respectively.
Our paper is organized as follows. In the next section we briefly present an introduction to the
UTAUT framework. Subsequent sections present our research model and hypotheses, discuss our data
collection method, and outline the data analysis procedure and the results of model testing. Finally we
conclude with a discussion of our findings and the implications for practice.
2
2. Theoretical Framework
Many MIS researchers have studied acceptance of new technologies over the past two decades.
The Theory of Reasoned Action (TRA) (Fishbein et al., 1975) and the Theory of Planned Behavior (TPB)
(Ajzen, 1985; Ajzen, 1987; Ajzen, 1991) have greatly informed work in determining behavioral
intentions. TRA states that antecedents of behavioral intentions are attitudes and subjective norms. TPB
added perceived behavioral control to the two antecedents of TRA and also added a direct relationship
between perceived behavioral control and actual behavior.
Davis (1989) adapted TRA and TPB to the MIS literature to create the widely-cited Technology
Acceptance Model (TAM) (Davis, 1989), which had dramatic influence on the MIS field. As of this
writing, Google Scholar reports over 9,000 citations to that single paper. TAM, which focuses on
technology acceptance, provides usefulness and ease of use as antecedents to behavioral intentions. Other
versions of TAM made slight changes to the constructs over the years.
After over a decade of research on TAM, Davis and colleagues proposed the Unified Theory of
Acceptance and Use of Technology (UTAUT) (Venkatesh et al., 2003), which has taken the place of the
various TAM models (see Figure 1). UTAUT has been used in many contexts to predict behavioral
intentions. The basic notion is that three antecedents will predict behavioral intentions: performance
expectancy (formerly perceived usefulness), effort expectancy (formerly perceived ease-of-use), and
social influence (not in the original TAM model). A direct antecedent of actual behavior is facilitating
conditions. Finally, control variables moderate the relationships of the three antecedents of intentions:
gender, age, experience, and voluntariness of use.
----------------------------------------- (Insert Figure 1 about here) -----------------------------------One of the most interesting aspects of these models is the construct “behavioral intentions,”
which has been used as a surrogate for actual behavior in hundreds of experiments, surveys, and field
studies alike in the MIS literature. Behavioral intentions have been used as surrogates for success at many
levels of granularity and have been assessed at the individual, group, and organizational levels of analysis.
A user’s behavioral intentions (to use software or information services) can serve as an excellent
summary variable that indicates content and/or design success.
3
TAM and UTAUT have been shown to enrich our understanding of computer-related adoption
behaviors in many contexts. UTAUT is therefore adopted in this study as a way in which to understand
adoption of continuous auditing tools. If there is a shortfall in adoption of such tools, it is important to
determine to what extent the various antecedents play a role. If the problem is effort expectancy, for
instance, training programs might be useful to reduce the usability barriers. If, on the other hand, the
problem is performance expectancy, demonstrations and performance statistics could be useful.
While the TAM and UTAUT models were proposed originally at the individual level of
acceptance, it is reasonable to assume that the organizational adoption of continuous auditing
technologies depend on most of the same constructs. Executives would need to be cognizant of
performance expectancy as well as effort expectancy in deciding to adopt the technology. Facilitating
infrastructure needs to be in place as well. Further, internal auditors even have social pressures from both
within and outside of their firms. Internal pressure points would include those peers and supervisors, as
UTAUT specifies. External pressures are likely to make this variable even more important in the case of
continuous auditing; the widespread adoption of these technologies by other firms can be naturally
considered to be a benchmark that they should follow so they do not fall behind. Not adopting what others
have adopted could even create exposure for a firm, such as the failure to detect fraud as a result of not
implementing advanced technologies such as continuous auditing.
There are promising organizational level variants of the control variables as well. As firms do not
have a gender, and age was intended as a personal variable, it is important to provide a control variable
that promises to capture essential control issues. We chose to insert annual sales as one control variable
that would serve to indicate ability to acquire the technology. We also use a voluntariness construct that is
identical to the one in UTAUT, because it will account directly for situations in which the use of such
technologies is dictated by another party.
3. Research Model and Hypotheses
As UTAUT specifies, and taking into account the aforementioned slight modifications to the
control variables as well, six constructs are expected to play a significant role in internal auditors’
willingness to adopt continuous auditing technology in an organizational context: effort expectancy,
performance expectancy, facilitating conditions, social influence, voluntariness of use and annual sales of
the company. In the remainder of this section, we define each of the determinants, specify the role of key
moderators (voluntariness and annual sales), and provide the theoretical justification for the hypotheses.
Figure 2 presents the research model.
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Effort Expectancy
Effort expectancy is defined as the degree of ease associated with the use of the system
(Venkatesh et al, 2003). With the advent of continuous auditing systems internal auditors can expect a
change in the nature of their work responsibilities from a traditionally reactive approach to a proactive
approach. Audit procedures used for transaction and compliance verification are automated in the
continuous auditing environment. The automation of transaction and compliance audit procedures shifts
the auditor's work to more complex audit objectives, such as dealing with estimate and judgment
verifications that require auditor judgment. Hence, the auditor's main role in the present continuous
auditing environment involves investigating irregularities/exceptions identified by the continuous auditing
system and dealing with audit procedures requiring judgment (Chan and Vasarhelyi, 2011). The more
seamlessly and effortlessly the auditors are able to transition into using the continuous auditing systems in
this new role, the higher will be their intentions to adopt them. Additionally, continuous auditing systems
employ (or will employ) enabling technologies including statistical methodologies such as belief
functions and neural networks, as well as technologies from computer science such as database and expert
systems, intelligent agents, and especially technologies for tagging data to facilitate transmission and
comparison, most notably XBRL and XBRLGL (Vasarhelyi et al., 2004). The internal auditors will not
only have to overcome the learning curve to become skillful at using continuous auditing systems, but
also find them easy and efficient to use. This is especially relevant since auditors will be frequently
interacting with the continuous auditing system. Given the complexities involved, the adoption of
continuous auditing systems will therefore be facilitated by the positive perceptions among internal
auditors with regard to effort expectancy. Therefore we hypothesize:
H1: Positive perceptions of effort expectancy will positively influence internal auditors’
intentions to adopt continuous auditing technology.
Performance Expectancy
Performance expectancy is defined as the degree to which an individual believes that using a
system will help achieve gains in job performance (Venkatesh et al., 2003). Continuous auditing is likely
to have many benefits such as continuous error and fraud detection, and the use of data analytics and data
modeling features (Vasarhelyi et al., 2004). Given these benefits, adoption of continuous auditing will be
facilitated by internal auditors’ perceptions of usefulness of the system in their work and the productivity
gains they can expect from it. Therefore, we argue that the extent to which internal auditors perceive
continuous auditing as being better than using its precursor, they are likely to have positive intentions to
adopt continuous auditing technology.
5
H2: Positive perceptions of performance expectancy will positively influence internal
auditors’ intentions to adopt continuous auditing technology.
Facilitating Conditions
Facilitating conditions are defined as the degree to which an individual believes that an
organizational and technical infrastructure exists to support use of the system. These conditions include
aspects of the technological and/or organizational environment that are designed to remove barriers to the
use of system (Venkatesh et al., 2003). This relates to technical, monetary and training support and the
resources available to the internal auditors in facilitating their adoption of the continuous auditing system.
As mentioned earlier, the use of continuous auditing systems is likely to involve learning of enabling
technologies and internal auditors who either possess the background knowledge or have access to the
resources required to learn will have more positive perceptions of facilitating conditions. The continuous
auditing systems also have to be compatible to systems auditors are already using. In our next hypothesis
we propose:
H3: Positive perceptions of facilitating conditions will positively influence internal auditors’
intentions to adopt continuous auditing technology.
Social Influence
Social influence is defined as the degree to which an individual perceives that important others
believe he or she should use the new system. Social influence as a direct determinant of behavioral
intention is represented as subjective norm, i.e., the explicit or implicit notion that the individual’s
behavior is influenced by the way in which they believe others will view them as a result of having used
the technology (Venkatesh et al., 2003). Internal auditors’ perception of social influence originates from
their superiors in the higher management and their peers. It is up to the internal auditors’ superiors to,
among other things, commit to the adoption of continuous auditing technology, obtain agreement from all
affected influential parties, and secure the organizational approval of funds needed to implement the
technology. We postulate in this hypothesis that:
H4: Positive perceptions of social influence will positively influence internal auditors’
intentions to adopt continuous auditing technology.
Moderating Variables
UTAUT calls for several moderators that are dependent on the particular adopter of the
technology. Because this is not personal technology, we replaced Gender and Age with Annual Sales.
6
Annual sales is likely to be a much better control variable, given that the gender and age of the potential
adopter or team should have no importance at all. Annual sales of the organization is considered to be a
more appropriate control factor. For instance, if effort expectancy (usability) or performance expectancy
(usefulness) is considered to be a potential problem, organizations with more resources would be better
able to afford training or customization to handle this problem.
The UTAUT model includes those moderators between the antecedents and behavioral intention
only for certain paths. UTAUT predicts that for performance expectancy, effort expectancy, social
influence, and facilitating conditions, gender only moderates the first three, age moderates all, and
experience moderates only the last three on behavioral intentions. Because we replaced gender, age, and
experience with annual sales, we hypothesize that annual sales will moderate all of those relationships.
H5a: Annual sales will moderate the relationship between effort expectancy and adoption.
H5b: Annual sales will moderate the relationship between performance expectancy and adoption.
H5c: Annual sales will moderate the relationship between facilitating conditions and adoption.
H5d: Annual sales will moderate the relationship between social influence and adoption.
The other moderating variable in UTAUT and our model is voluntariness of use. According to
Figure 1, voluntariness of use only moderates the relationship between social influence and behavioral
intention. We maintain that aspect of UTAUT in our research model. Therefore:
H6: Voluntariness of use will moderate the relationship between social influence and adoption.
----------------------------------------- (Insert Figure 2 about here) ------------------------------------
4. Data and Sample
We conducted an online survey by e-mailing respondents a link to our electronic survey site2. The
survey was e-mailed on our behalf for this study by the Institute of Management Accountants (IMA) to
their worldwide members whose membership profile listed one of the following responsibilities: internal
auditing, risk management, information systems or general accounting3. The survey e-mail explained the
nature of the survey and asked respondents to complete the survey provided they were knowledgeable on
2
The Qualtrics tool was used for this survey.
The IMA has substantially more members who profile themselves as “accountants” than they do “internal auditors,” “risk management,” or
“information systems.” Hence, while our survey captured practitioners in the latter three groups, we also captured practitioners in the
“accountants” group who either had direct knowledge of their company’s internal audit operations or passed on the email to someone else in
the company who did.
3
7
their company’s continuous auditing efforts and, if not, to forward the e-mail to someone within their
company’s internal audit function.
The number of surveys e-mailed was 9,013 and the number of usable responses was 234 (a
response rate of 2.60%). Of those 234 usable responses, the percentage breakdown by regional
geographic location of company operations was: North America 61%; Middle East 27%; Asia 6%;
Europe 4%; others 2%. These percentages are comparable to the IMA’s worldwide membership
breakdown of North America 72%; Middle East 16%; Asia 8%; Europe 3%; others 1%.4
The first question in the survey posed to respondents was “What is the current state of Continuous
Auditing in your company?” The four possible responses and the percentage of respondents selecting
each response were:

Fully operational in one or more of our company's systems – 19%

In place but not yet fully developed – 23%

Not implemented yet but scheduled to be implemented in future – 16%

Not implemented and no plans for future implementation – 42%
The percentages above can be compared to the three previously cited international accounting
firm surveys that were described previously and shown in Table 1. Such a comparison reveals that from
the PwC (2007) survey to the present survey in this study, the percentage of companies that have either
fully operational continuous auditing systems or systems in place but not yet fully developed (the first
two bulleted categories above) has not increased much, if at all.
After the introductory question, respondents were asked to answer a set of approximately 40
questions. The questions were of three main types: (1) company profile questions such as annual sales
and geographic location of operations, (2) questions regarding current use of and future plans for
continuous auditing, and (3) questions structured on the UTAUT technology adoption framework
described in the last section. The actual survey questions for this latter group are shown in Table 2.
Survey responses to these questions enabled us to analyze the current state of adoption of continuous
auditing technology using the UTAUT framework. We next describe the method and analysis.
----------------------------------------- (Insert Table 2 about here) ------------------------------------
4
IMA membership profile information as of April 30, 2011.
8
5. Research Method
To estimate the paths between the constructs shown in our research model (Fig. 2), and thereby
test the propositions advanced previously, we used partial least squares (PLS) which is a powerful
multivariate analysis technique. PLS is useful for analyzing structural equations with latent variables. It is
similar to LISREL, which is probably the best known of the second generation statistical techniques, in
the sense that the measurement and structural (or theoretical) models are analyzed simultaneously.
However, unlike LISREL, PLS relies on ordinary least squares estimation techniques to solve the
equations (Compeau and Higgins, 1995). PLS is most appropriate when sample sizes are small, when
assumptions of multivariate normality and interval scaled data cannot be made, and when the researcher is
primarily concerned with prediction of the dependent variable (Birkinshaw et al., 1995). The major
benefits of PLS include robustness for small to medium sample sizes and fewer constraints on the data
(e.g., normality assumptions) compared to covariance-based methods such as LISREL (Wakefield, et al.
2008). Simulation studies have also shown PLS to be robust against inadequacies such as multicollinearity, skewness and omission of regressors (Cassel et al., 1999).
In PLS all relationships are modeled simultaneously, sharply reducing concerns about
multicollinearity (Inkpen and Birkenshaw, 1994). The path coefficients obtained from a PLS analysis are
standardized regression coefficients, while the loadings of items on individual constructs are factor
loadings. Factor scores created using these loadings are equivalent to weighted composite indices. Thus,
PLS results can be easily interpreted by considering them in the context of regression and factor analysis
(Birkinshaw et al., 1995). The R2 values are used to assess the proportion of variance in the endogenous
constructs which can be accounted for by the antecedent constructs (Compeau and Higgins, 1995).
Generally, PLS results are presented in two stages. In the first stage, the researcher ensures that the
measures used as operationalizations of the underlying constructs are both reliable and valid. Once
convinced of the adequacy of the measurement model, the researcher proceeds to the second stage and
interprets the resulting model coefficients (Birkinshaw et al., 1995).
6. Data Analysis and Results
6.1 Measurement model
As evident in Table 3, all average variance extracted (AVE) values are above .50 and composite
reliability coefficients are above .70. This indicates that the measurements are reliable and the latent
constructs account for more than 50 percent variance in the items. The loadings are also in the acceptable
range and all the t-values suggest that they are significant at the .01 level.
9
----------------------------------------- (Insert Table 3 about here) ------------------------------------
Table 4 suggests that our measurement model demonstrates sufficient discriminant validity. If the
square root of the AVE is greater than all of the inter-construct correlations, it is evidence of sufficient
discriminant validity (Chin, 1998). This is the case with all the AVE values shown in Table 4.
----------------------------------------- (Insert Table 4 about here) ------------------------------------
To further assess validity of our measurements, a cross loading table (Table 5) was also
constructed as suggested by Gefen et al. (2000). If each item loading in the table is higher on its assigned
construct than on other constructs, it is evidence of adequate convergent and discriminant validity. As can
be seen in Table 5, all the diagonal elements are high and also greater than off diagonal elements
suggesting adequate convergent and discriminant validity of our measures. We noted that SI3 and SI4
seem to cross-load on facilitating conditions, and all facilitating conditions items seem to cross-load on
SI. This issue will be discussed and handled in a supplementary analysis that includes a revised set of
constructs for the UTAUT model.
----------------------------------------- (Insert Table 5 about here) ------------------------------------
6.2 Common method bias
Common method biases may occur if the predictor and the criterion variables share a common
method. In such a scenario, the common method may exert a systematic effect on the observed
correlations between measures. Thus, at least partially, common method biases may pose a rival
explanation for the observed correlations between the measures. Similarly, a common rater bias may
occur due to any artifactual covariance between predictor and the criterion variable produced by the fact
that the respondent providing the measure of these variables is the same (Podsakoff et al., 2003). In order
to rule out any rival explanations due to common method bias, we follow Liang et al. (2007) and include
in the PLS model a common factor whose indicators include all the principal constructs’ indicators and
calculate each indicators’ variances substantively explained by the principal construct and by the method.
The evidence for common method bias can be obtained by examining the statistical significance
of the factor loadings on the method factor and comparing the variances of each observed indicator
explained by its substantive construct and the method factor (Williams et al., 2003). The squared values
10
of substantive and method factor loadings are interpreted as variances explained by the substantive and
method constructs, respectively. If the method factor loadings are insignificant and substantive variances
are substantially greater than the method variances, then we may conclude that common method bias is
unlikely to be of any concern (Liang et al., 2007). As seen in Table 6, the average substantive variance is
.768 while the average method variance is .009. The ratio of substantive variance to method variance is
thus about 85:1. In addition, most method factor loadings are not significant. Therefore we argue that
common method bias is unlikely to be of concern for this study.
----------------------------------------- (Insert Table 6 about here) ------------------------------------
6.3 Hypothesis testing
Figure 3 presents the estimates obtained from the PLS analysis. Solid lines in the figure represent
significant paths while insignificant paths are represented by dashed lines. The R2 value of .443 indicates
that a significant amount of variance (44.3%) is explained by the model. The effect of effort expectancy
on adoption was significant at the .05 level (b = .152), providing support for H1. Our analysis did not find
support for H2 or H3 which represent the effects of performance expectancy and facilitating conditions
on adoption, respectively. While these latent variables are significant predictors of adoption when
UTAUT is applied in an individual context (Venkatesh et al., 2003), in our organizational model they
were not significant predictors. Social influence was found to have a significant impact on adoption
intentions of internal auditors (b = .331, p < .01) providing support for H4.
As for H5(a)–(d), Figure 3 shows that the links for H5(a), (b) and (d) are insignificant thereby
failing to confirm the moderating role of annual sales on effort expectancy, facilitating conditions and
social influence, respectively. However, the link for H5(c), performance expectancy, was significant at
the .01 level (b = -.141). Finally, the link for H6 was significant at the .01 level (b = -.253) thereby
lending support to the role of voluntariness as a moderator for the relationship between social influence
and adoption.
----------------------------------------- (Insert Figure 3 about here) ------------------------------------
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6.4 Additional observations
In addition to the main results described in the last subsection, we noted several other key
observations from the survey responses that are worth pointing out. One concerns respondents’ perceived
benefits of continuous auditing and a second, related one is the perceived focus of the continuous auditing
system for respondents’ organizations. Overall, respondents view accuracy as the most important benefit,
followed in order of importance by timely communication, audit efficiency, timely analysis, and cost
savings. The responses to those questions differ to a small extent based on whether the program is fully or
only partially in place (Table 7).
----------------------------------------- (Insert Table 7 about here) ------------------------------------
In terms of the focus of the continuous auditing system, survey respondents overall rated the
monitoring of fraudulent activities as the most important focus of their continuous auditing system,
followed in order of importance by risk monitoring, testing of controls, detail testing of transactions, and
performance evaluation. The responses are very similar whether or not the systems are in place or are
expected in the future (Table 8). These two sets of responses imply that respondents believe that a
continuous auditing system’s ability to provide audit coverage for 100% of transactions will increase the
accuracy of accounting information while contributing to reduced risk with an emphasis on fraud risk.
----------------------------------------- (Insert Table 8 about here) ------------------------------------
Several survey questions concern the ownership and performance of the continuous auditing
function. Table 9 shows that the internal audit department has responsibility for continuous auditing in
about three-quarters of respondents’ companies; small minorities of companies assign the responsibility
to an IT group or an accounting/finance group. A comparison of companies with fully implemented
continuous auditing systems to companies in which a continuous auditing system is in place but not yet
fully implemented reveals that the IT group is primarily responsible a greater percentage of the time in the
latter (8.2% v. 13.2%). This seems to indicate that in early implementation stages IT groups are given
initial responsibility for the continuous auditing system, and once the system is fully implemented,
responsibility is transferred to internal auditing.
----------------------------------------- (Insert Table 9 about here) ------------------------------------
12
The performance of continuous auditing, however, as depicted in Table 10, does not fully mirror
responsibility or ownership. While in the majority of cases internal auditing is assigned the performance
(57.7% overall), this proportion is less than the three-quarters of internal auditing departments that are
assigned the responsibility for continuous auditing as per the preceding paragraph. In a minority but still
significant number of cases, IT management and risk management perform continuous auditing (18.9%
overall and 16.3% overall, respectively). This suggests that continuous auditing technology is sufficiently
sophisticated and complex that in some companies IT management is designated as the party to perform
continuous auditing, while in other companies continuous auditing is seen is a vital risk management tool
and
accordingly
risk
management
is
assigned
its
performance.
Additionally,
as
with
responsibility/ownership, we see a higher percentage assigned to IT in companies in which the continuous
auditing system is in place but not yet fully implemented compared to companies in which the continuing
system is fully operational (16.3% versus 25.5%).
----------------------------------------- (Insert Table 10 about here) -----------------------------------The term “continuous auditing” tends to conjure up images of perpetual, never-ending auditing,
in both concept and practice. However, this is frequently not the case. As stated in Chan and Vasarhelyi
(2011):
“Auditing continuously or in real time may seem ideal. However, the real
time audit can impact the operation of the accounting information system and may
not always be cost-effective. As a result, real time continuous auditing will tend to
occur in high risk business processes. For example, in industrial firms it may be
prudent to continuously audit the higher risk treasury disbursement process in real
time. On the other hand, it may be sufficient to audit the lower risk prepayment
expense process in periodic or frequent cycles.” (Chan and Vasarhelyi, 2011)
Our survey results bear this out: overall only 13.2% of respondents’ companies perform continuous
auditing real-time; the majority perform continuous auditing monthly or quarterly (Table 11). The
percentages of continuous auditing systems that fall into each category of time frame shown in Table 11
are quite similar to findings in PwC (2006).
----------------------------------------- (Insert Table 11 about here) ------------------------------------
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We found that companies with fully-developed and in place (partially developed but not fully in
place) continuous auditing systems developed their technology within their own company or insourced it
in 69.8% (52.4%) of cases, outsourced the development of their technology 19.0% (27.0%) of the time,
and purchased their technology from an outside vendor about 11.2% (20.6%) of the time (Table 12).
Hence, most companies choose in-house development of their continuous auditing systems, but
significant proportions of companies outsource or purchase from an outside vendor. There does not seem
to be a “one size for all formula” for acquiring continuous auditing technology. This suggests that a
number of factors determine how a particular company approaches their acquisition of continuous
auditing technology.
----------------------------------------- (Insert Table 12 about here) ------------------------------------
Finally, our questions about future plans for continuous auditing elicited interesting responses.
Table 13 illustrates the plans of those respondents without continuous auditing to move to
implementation. The middle two time-frames given were most popular: 64% of the subjects chose the
next 12 to 24 months. The remainder was evenly split between the next 6 months and after 24 months.
Respondents also asked whether the economic climate had an effect on implementation plans. Only 9%
answered “yes” and 4% answered “somewhat.” The bulk of respondents (87% of those who have not
implemented continuous auditing) replied “not at all” (Table 14). Table 15 provides a breakdown of
reasons for failure to implement continuous auditing. By far the most popular answer was “we have not
looked into continuous auditing yet” (76%).
-------------------------------- (Insert Table 13, 14 and 15 about here) ----------------------------
7. Exploring a Revised Model
Reflecting on the strong effort expectancy and social influence paths and the two troublesome
items in the social influence construct, we revised the instruments. Even though the discriminant and
convergent validation procedures as suggested by Gefen et al. (2000) appeared successful, we recognized
some overlap between the facilitating conditions construct and items SI3 and SI4. Specifically, the two
items (“senior management has been helpful…” and “my organization has supported…”) appear to be
highly related to facilitating conditions items as shown in Table 2.
14
In addition, we explored expanding the use of the “voluntary” construct to act as a moderator for
all four main effects in the UTAUT model as well. It was not difficult to construct theoretical arguments
for each moderating effect. If a system is mandated, then all of the main effects are obviously
unimportant. Even if the firm would consider the system to be hard to use, not very useful, or not
supported, the system would still be adopted. Therefore, we see a strong argument to promoting
“voluntary” as a mediator of all of the main effects. We performed exploratory analysis of a revised
model by moving the two items to the facilitating conditions construct and making the “voluntary”
construct a moderator of all of the main effects.
As we expected, the item loadings of SI3 and SI4 were much higher as part of facilitating
conditions. Table 16 provides a revised measurement model, Table 17 a revised correlation table, and
Table 18 a revised list of item loadings. Our conclusion is that the measures appear to exhibit stronger
discriminant and convergent validity using the revised item structure.
---------------------------------- (Insert Table 16, 17 and 18 here) ---------------------------
The revised analysis resulted in a model that was much easier to explain (Figure 4). Effort
expectancy and facilitating conditions were significant determinants of adoption. At first, it seemed
surprising that performance expectancy was not a main effect. But the negatively-valenced moderator of
annual sales helped clarify what seemed anomalous. In a firm with more modest sales, performance
expectancy is quite important. However, in large firms that can afford continuous auditing, it does not
matter if the software seems less useful. Deeper pockets seem to provide a less cautious approach.
More importantly, the social influence main effect disappears. Instead, “voluntary” moderates the
relationship between social influence and adoption. If a system is mandatory, social influence does not
have a significant path, but when it is voluntary, auditors look to their colleagues for guidance.
----------------------------------------- (Insert Figure 4 about here) ------------------------------------
8. Discussion and Conclusions
In spite of the fact that the concept of continuous auditing was first introduced over two decades
ago, and that the concept has garnered a considerable amount of attention in both the academic and
professional literature, to date continuous auditing has been adopted to a limited extent, and almost
exclusively in the internal audit domain. While past surveys of CAEs and other internal audit executives
15
have indicated that plans for the implementation of continuous auditing were robust (Grant Thornton,
2011; KPMG, 2010; PwC, 2006), the results of this study’s survey indicate that progress has been made,
but there is still continued lag in the implementation of continuous auditing systems.
This study explores this lag in the adoption of continuous auditing, and seeks answers to why this
is the case. We surveyed internal auditors worldwide about the current state of adoption of continuous
auditing technology in their companies. Through application of the Unified Theory of Acceptance and
Use of Technology (UTAUT) framework, and after making some important but minor alterations to the
constructs and model, we analyzed respondents’ answers to find that the significant factors leading to
adoption of continuous auditing technology are Effort Expectancy, i.e., the ease and clarity of use, and
Facilitating Conditions, i.e., organizational support through resources, knowledge, and assistance.
In addition, the level of annual sales volume seems to moderate another important construct,
performance expectancy. Large firms tend to adopt without questioning performance expectancy but
adoption in firms with lower sales volume tend to require that the software will be useful. Another
moderator, the extent to which the system is perceived to be voluntary or mandatory, also moderates the
effect of social influence on adoption, as UTAUT predicts.
Given that respondents view accuracy as the most important benefit of continuous auditing, the
UTAUT results regarding Effort Expectancy seem quite positive. If internal auditors place top priority on
accuracy and, at the same time, believe that continuous auditing technology is uncomplicated to use, one
can logically conclude that they would be quite supportive of plans for its implementation and further use.
This bodes well for future adoption and expanded utilization of continuous auditing technology. It
therefore becomes a matter of companies’ commitment to the use of the technology: an organization that
decides to implement continuous auditing, or expand its use, will likely be able to count on little
resistance and, more likely, strong support from its internal auditors. Indeed, the result regarding Social
Influence bears this out – it almost seems as if internal auditors are ready and waiting for their company’s
top management to commit to continuous auditing.
16
References
Ajzen, I. From intentions to actions: A theory of planned behavior. In: Behavior, J. Kuhl & J. Beckman,
Eds. Action-control: From cognition to behavior. Springer, Heidelberg, Germany. 1985: 11-39.
Ajzen, I. Attitudes, traits, and actions: Dispositional prediction of behavior in personality and social
psychology. In: Berkowitz, L., Ed. Advances in experimental social psychology. Academic Press, New
York, 1987: Vol. 20, 1–63
Ajzen, I. The theory of planned behavior. Organizational Behavior and Human Decision Processes 1991:
50 (2), 179-211.
Alles, M., G. Brennan, A. Kogan, and M. A. Vasarhelyi. Continuous Monitoring of Business Process
Controls: A Pilot Implementation of a Continuous Auditing System at Siemens. International Journal of
Accounting Information Systems 2006; 7(2): 137-61.
_________, G., A. Kogan and M.A. Vasarhelyi. Putting continuous auditing theory into practice: Lessons
from two pilot implementations. Journal of Information Systems 2008; 22(2): 195-214.
Birkinshaw, J., Morrison, A. and Hulland H. Structural and Competitive Determinants of a Global
Integration Strategy. Strategic Management Journal 1995; 16 (8), 637 - 655.
Brown, C., J. Wong, A. Baldwin. A Review and Analysis of the Existing Research Streams in Continuous
Auditing. Journal of Emerging Technologies in Accounting 2007; 4: 1-28.
Cassel, C., Hackl, P., & Westlund, A. H. Robustness of partial least-squares method for estimating latent
variable quality structures. Journal of Applied Statistics 1999; 26, 435–446.
Chan, D. and M. Vasarhelyi. Innovation and practice of continuous auditing. International Journal of
Accounting Information Systems 2011; 12: 152-60.
Chin, W. W. “The Partial Least Squares Approach to Structural Equation Modeling,” in Modern Methods
for Business Research, G. A. Marcoulides (ed.), Lawrence Erlbaum Associates, Hillsdale, NJ, 1998, 295336.
Compeau, D. R., and Higgins, C. A. “Application of social cognitive theory to training for computer
skills,” Information Systems Research 1995; 6(2): 118-143.
Davis, F.D. Perceived usefulness, perceived ease of use, and user acceptance of information technology.
MIS Quarterly: Management Information Systems 1989: 13 (3), 319-339.
Debreceny, R. S., G. L. Gray, J. Jun-Jin Ng, K. Siow-Ping Lee, and W. Yau. Embedded Audit Modules in
Enterprise Resource Planning Systems: Implementation and Functionality. Journal of Information
Systems 2005; 19 (2): 7-27.
Fishbein, M. and I. Ajzen. Belief, attitude, intention, and behavior. Addison-Wesley, Reading, MA. 1975.
Flowerday, S. and R. von Solms. Continuous auditing: verifying information integrity and providing
assurances for financial reports. Computer Fraud & Security 2005; July 2005: 12-16.
17
_________, A. Blundell and R. von Solms. Continuous auditing technologies and models: A discussion.
Computers & Security 2006; 25: 325-331.
Gefen, D., Straub, D., and Boudreau, M.-C. “Structural Equation Modeling and Regression: Guidelines
for Research Practice,” Communications of the AIS 2000; (4:7).
Grant Thornton. Looking to the future: perspectives and trends from internal audit leaders. Grant
Thornton LLP 2011.
Groomer, S. and U. Murthy. Continuous auditing of database applications: an embedded audit module
approach. Journal of Information Systems 1989; 3(2): 53-69:
Hermanson, D., B. Moran, C. Rossie and D. Wolfe. Continuous Monitoring of Transactions to Reduce
Fraud, Misuse, and Errors. Journal of Forensic Accounting 2006; VII: 17-30.
Hunton, J. , A. Wright and S. Wright. Continuous Reporting and Continuous Assurance: Opportunities
for Behavioral Accounting Research. Journal of Emerging Technologies in Accounting 2004; 1: 91-102.
_________, E. Mauldin and P. Wheeler. Potential Functional and Dysfunctional Effects of Continuous
Monitoring. The Accounting Review 2008; 83(6): 1551-1569.
_________, __________ and _________. Continuous monitoring and the status quo effect. International
Journal of Accounting Information Systems 2010; 11(3): 239-252.
Inkpen, A. and Birkinshaw, J.. “International Joint Ventures and Performance: an Interorganizational
Perspective,” International Business Review, 1994; 3(3), 201-217.
Jans, M., M. Alles and M. Vasarhelyi. Process Mining of Event Logs in Auditing: Opportunities and
Challenges. 2010; Working Paper.
Kogan, A., E. F. Sudit, and M. A. Vasarhelyi. Continuous online auditing: A Program of research. Journal
of Information Systems. 1999; 13 (2): 87-103.
KPMG. Continuous auditing and monitoring: Are promised benefits now being realised? 2010; KPMG
LLP.
KPMG International (KPMG). What is Driving Continuous Auditing & Continuous Monitoring Today?
2010; KPMG International Cooperative.
Kuhn, J. and S. Sutton. Learning from WorldCom: Implications for Fraud Detection through Continuous
Assurance. Journal of Emerging Technologies in Accounting 2006; 3: 61-80.
_________ and _________. Continuous Auditing in ERP System Environments: The Current State and
Future Directions. Journal of Information Systems 2010; 24(1): 91-112.
Liang, H., Saraf, N., Hu, Q., and Xue, Y. Assimilation of Enterprise Systems: The Effect of Institutional
Pressures and the Mediating Role of Top Management. MIS Quarterly 2007; 31(1), 59-87.
Podsakoff, P., MacKenzie, S., Lee, J., and Podsakoff, N. Common Method Biases in Behavioral
Research: A Critical Review of the Literature and Recommended Remedies. Journal of Applied
Psychology 2003; 88:5: 879-903.
18
PricewaterhouseCoopers (PwC). State of the internal audit profession study: Continuous auditing gains
momentum. 2006; Available at: http://www.pwc.com/en_BE/be/systems-process-assurance/pwc-state-ofinternal-audit-2006.pdf.
_________________________ . State of the internal audit profession study: Pressures build for continual
focus
on
risk.
2007;
Available
at:
http://www.pwc.com/en_US/us/internalaudit/assets/state_internal_audit_profession_study_07.pdf.
Rezaee, Z., A. Sharbatoghlie, R. Elam and P. McMickle. Continuous Auditing: Building Automated
Auditing Capability. Auditing: A Journal of Practice & Theory 2002; 21(1): 147-163.
Searcy, D., J. Woodroof, and B. Behn. Continuous Audit: The Motivations, Benefits, Problems, and
Challenges Identified by Partners of a Big 4 Accounting Firm. 2003; 36th Annual Hawaii International
Conference on System Sciences, 7: 210
Vasarhelyi, M., M. Alles, and A. Kogan. Principles of analytic monitoring for continuous assurance.
Journal of Emerging Technologies in Accounting 2004; 1 (1): 1-2l.
_________ and F. Halper. The continuous audit of online systems. Auditing: A Journal of Practice &
Theory 1991; 10 (1): 110-128.
_________, A. Kogan and M. Alles. Would Continuous Auditing Have Prevented the Enron Mess? The
CPA Journal July 2002: 80.
Venkatesh, V., Morris, M. G., Davis, G. B. and Davis, F. D. User acceptance of information technology:
Toward a Unified View. MIS Quarterly 2003; 27 (3), 425-478.
Wakefield, R.L., D.E. Leidner, and G. Garrison, A model of conflict, leadership, and performance in
virtual teams. Information Systems Research 2008; 19:4, 434–455.
Williams, L. J., Edwards, J. R., and Vandenberg, R. J. Recent Advances in Causal Modeling Methods for
Organizational and Management Research. Journal of Management 2003; 29:6, 903-936.
19
Table 1 - Summary of Recent Surveys’ Findings of Continuous Auditing Technology Adoption
PwC 20071
13%
37%
31%
19%
100%
KPMG 2010
7%
13%
3%
77%
100%
Fully Operational in one or more of our company's systems.
In place but not yet fully developed.
Not implemented yet but scheduled to be implemented in future.
Not implemented and no plans for future implementation.
Total
1
PwC 2007 = PricewaterhouseCoopers 2007
2
GT 2011 = Grant Thornton 2011
3
published survey does not distinguish between “fully operational” and “not fully developed”.
4
published survey does not distinguish between “no plans at all” and “plans but not yet implemented”.
GT 20112
-3
33%3
-4
67%4
0%
20
Table 2 – Constructs and Corresponding Survey Questions based on UTAUT framework
Construct
Indicator
EE1
EE2
EE3
EE4
PE1
PE2
PE3
PE4
Survey Question*
Interacting with the Continuous Auditing system is generally clear and understandable
It is generally easy to become skillful at using the Continuous Auditing system
Effort
Expectancy
I (we) find the Continuous Auditing system easy to use
Learning to operate the Continuous Auditing system is easy for me (us)
A Continuous Auditing system is useful in my (our) job
A Continuous Auditing system enables me (us) to accomplish tasks more quickly
Performance
A Continuous Auditing system increases my (our) productivity
Expectancy
A Continuous Auditing system increases my (our) chances of improving my (our)
financial position
FC1
My (our) company has the resources necessary to use the Continuous Auditing system
FC2
My (our) company has the knowledge necessary to use the Continuous Auditing
system
Facilitating
Conditions
FC3
The Continuous Auditing system is compatible with other systems I (we) use
FC4
A specific person (or group) is available for assistance with the Continuous Auditing
system difficulties
SI1
People or parties who influence my (our) behavior think that I (we) should use the
Continuous Auditing system
SI2
People or parties who are important to me (us) think that I (we) should use the
Social
Continuous Auditing system
Influence
SI3
Senior management has been helpful in the use of the Continuous Auditing system
SI4
In general, my (our) organization has supported the use of the Continuous Auditing
system
DV1
I (we) intend to use the Continuous Auditing system in the foreseeable future
DV2
I predict I (we) would use the Continuous Auditing system in the coming future
Adoption
DV3
My (our) use of Continuous Auditing is very likely to occur soon
1 = less than $1 million; 2 = $1 million to $10 million; 3 = $10 million to $100
Annual Sales Sales
million; 4 = $100 million to $1 billion; 5 = more tan $1 billion
Voluntariness Voluntary 1= Voluntary; 2 = Mandated
(1 = Strongly Disagree; 5 = Strongly Agree)
* The questions shown above are the set of questions asked of survey respondents who indicated they
currently have a continuous auditing system, either fully operational or in place but not yet fully
implemented. The questions were similar but worded slightly differently for those participants who
indicated that they had a scheduled but not yet implemented continuous auditing system or did not have
any plans for a system.
21
Table 3 - Loadings of the Indicator Variables (AVE) (Composite Reliability)
Construct
Effort
Expectancy
(.694)(.900)
Performance
Expectancy
(.739)(.918)
Facilitating
Conditions
(.713)(.908)
Social Influence
(.710)(.907)
Adoption
(.846)(.942)
Annual Sales
Voluntariness
Indicator
EE1
EE2
EE3
EE4
PE1
PE2
PE3
PE4
FC1
FC2
FC3
FC4
SI1
SI2
SI3
SI4
DV1
DV2
DV3
Sales
Voluntary
Mean
3.610
3.670
3.520
3.630
3.920
3.670
3.650
3.780
3.380
3.440
3.310
3.270
3.360
3.490
3.540
3.570
3.230
3.330
3.100
3.400
1.220
SD
.738
.790
.765
.755
.835
.964
.943
.978
1.030
.977
.904
.971
.853
.826
.993
.967
1.114
1.150
1.198
1.280
.415
Loading
.837
.808
.857
.829
.824
.900
.868
.843
.867
.844
.833
.834
.828
.808
.872
.860
.925
.924
.911
1
1
t-value
24.208
19.847
29.844
20.705
25.568
51.707
31.637
30.594
36.932
29.453
31.714
28.378
26.095
18.502
47.838
34.815
56.959
64.594
49.480
n/a
n/a
22
Table 4 – Correlations among Major Constructs
Adoption
Effort Expectancy (EE)
Facilitating Conditions (FC)
Performance Expectancy (PE)
Social Influence (SI)
Annual Sales (Sales)
Voluntariness (Voluntary)
Diagonals are AVE values.
* p < .05; **p < .01 (2-tailed)
Adoption
0.846
0.416**
0.499**
0.441**
0.568**
0.085
0.311**
EE
FC
PE
0.694
0.489**
0.616**
0.501**
0.060
0.106
0.713
0.433**
0.713**
0.269**
0.345**
SI
Sales
0.739
0.571** 0.710
0.078
0.120
1.000
0.183** 0.267** 0.177*
Voluntary
1.000
Table 5 - Item Loadings and Cross Loadings
Adoption
DV1
0.925
DV2
0.924
DV3
0.911
0.379
EE1
0.328
EE2
0.347
EE3
0.325
EE4
0.458
FC1
0.384
FC2
0.409
FC3
0.430
FC4
0.350
PE1
0.400
PE2
0.353
PE3
0.406
PE4
0.425
SI1
0.390
SI2
0.540
SI3
0.531
SI4
0.085
Sales
0.311
Voluntary
EE
0.385
0.406
0.357
0.837
0.808
0.857
0.829
0.381
0.328
0.473
0.467
0.498
0.546
0.557
0.516
0.411
0.425
0.427
0.430
0.060
0.106
FC
0.459
0.440
0.478
0.457
0.337
0.456
0.369
0.867
0.844
0.833
0.834
0.356
0.390
0.350
0.387
0.544
0.468
0.645
0.706
0.269
0.345
PE
0.420
0.384
0.412
0.581
0.524
0.473
0.466
0.363
0.335
0.389
0.372
0.824
0.900
0.868
0.843
0.485
0.478
0.531
0.435
0.078
0.183
SI
0.523
0.537
0.509
0.533
0.357
0.410
0.353
0.589
0.575
0.644
0.603
0.487
0.505
0.466
0.501
0.828
0.808
0.872
0.860
0.120
0.267
Sales
0.028
0.073
0.131
0.032
0.064
0.064
0.041
0.261
0.242
0.219
0.187
0.062
0.115
0.058
0.032
0.088
0.031
0.086
0.179
1.000
0.177
voluntary
0.272
0.240
0.343
0.043
0.119
0.134
0.063
0.297
0.282
0.316
0.270
0.231
0.133
0.101
0.166
0.211
0.132
0.235
0.298
0.177
1.000
23
Table 6 - Common Method Bias Analysis
Construct
Effort Expectancy
Performance
Expectancy
Facilitating
Conditions
Social Influence
Voluntariness
Annual Sales
Adoption
Indicators
EE1
EE2
EE3
EE4
PE1
PE2
PE3
PE4
FC1
FC2
FC3
FC4
SI1
SI2
SI3
SI4
Voluntary
Sales
DV1
DV2
DV3
Average
*p < .05; **p < .01; ***p < .005
Substantive Factor
Loading (R1)
0.671***
0.840***
0.893***
0.926***
0.825***
0.912***
0.916***
0.780***
0.895***
0.966***
0.748***
0.767***
0.951***
0.995***
0.721***
0.715***
1.000***
1.000***
0.926***
0.932***
0.902***
R12
0.450
0.706
0.797
0.857
0.681
0.832
0.839
0.608
0.801
0.933
0.560
0.588
0.904
0.990
0.520
0.511
1.000
1.000
0.857
0.869
0.814
0.768
Method Factor
Loading (R2)
0.198**
-0.045
-0.037
-0.114*
0.002
-0.012
-0.052
0.066
-0.042
-0.14**
0.109*
0.078
-0.117
-0.185**
0.152*
0.143*
0.000
0.000
0.001
-0.008
0.008
0.000
R22
0.039
0.002
0.001
0.013
0.000
0.000
0.003
0.004
0.002
0.020
0.012
0.006
0.014
0.034
0.023
0.020
0.000
0.000
0.000
0.000
0.000
0.009
24
Table 7 - Survey Responses: Most important benefits of Continuous Auditing
Mean Rating*
CA fully in place
CA partially in place
Accuracy
5.90
6.08
Timely communication
5.60
5.87
Audit efficiency
5.43
5.82
Timely analysis
5.45
5.79
5.44
Cost savings
*Rating scale: (1 = Not all important; 7 = Extremely Important)
5.53
Table 8 - Survey Responses: Focus of company’s Continuous Auditing
Mean Rating*
Currently in place
To be implemented in future
Monitoring possible fraudulent activities
5.76
5.84
Risk monitoring
5.63
5.82
Testing of controls
5.64
5.74
Detail testing of transactions
5.52
5.45
4.93
Performance evaluation
*Rating scale: (1 = Not all important; 7 = Extremely Important)
4.92
Table 9 - Survey Responses: Ownership of Continuous Auditing
Internal auditing
IT group
Accounting/finance function
Others
Total
CA fully in place
78.6%
8.2%
9.2%
4.1%
100.0%
CA partially in place
73.7%
13.2%
7.9%
5.3%
100.0%
Overall
77.2%
9.6%
8.8%
4.4%
100.0%
Table 10 - Survey Responses: Responsibility for Performing Continuous Auditing
Internal auditing
IT management
CA being performed currently
60.3%
16.3%
Risk management
Other
Total
15.6%
7.8%
100.0%
CA to be performed in future Overall
50.9%
57.7%
25.5%
18.9%
18.2%
5.5%
100.0%
16.3%
7.1%
100.0%
25
Table 11 - Survey Responses: Continuous Auditing Frequency
Real time
By the minute
Hourly
Daily
Weekly
Monthly
Quarterly
Other
Total
CA fully in place
11.2%
1.0%
1.0%
13.3%
6.1%
29.6%
34.7%
3.1%
100.0%
CA partially in place
18.4%
0.0%
0.0%
7.9%
15.8%
26.3%
26.3%
5.3%
100.0%
Overall
13.2%
0.7%
0.7%
11.8%
8.8%
28.7%
32.4%
3.7%
100.0%
Table 12 - Survey Responses: Source of Development of Continuous System by Percentage
Developed within your company or insourced
Developed by an outsourcing partner
Purchased from an outside vendor
CA fully in place
69.8%
19.0%
11.2%
100%
CA partially in place
52.4%
27.0%
20.6%
100%
Table 13 - Survey Responses: Plans for Future Implementation in:
The next 6 months
The next 12 months
The next 24 months
After 24 months
Total
16%
32%
32%
21%
100%
Table 14 - Survey Responses: Have Recent Economic Events
Company’s Plans to Involve in Continuous Auditing?
Yes
Somewhat
Not at all
Total
9%
4%
87%
100%
26
Table 15 - Survey Responses: Reasons for having No
Plans for Implementation*
8%
We do not believe it would benefit us much
20%
We lack the expertise
15%
We lack the technology
17%
We do not have the necessary funds
76%
We have not looked into Continuous Auditing yet
11%
Other
* Out of 234 survey respondents, 98 indicated they had not implemented continuous auditing and had no
plans for future implementation. Those 98 respondents explained their company’s non-implementation by
selecting the answers in the table.
Table 16 - Revised Model Loadings of the Indicator Variables (AVE) (Composite
Reliability)
Construct
Effort
Expectancy
(.694)(.900)
Performance
Expectancy
(.739)(.918)
Facilitating
Conditions
(.666)(.923)
Social Influence
(.874)(.932)
Adoption
(.846)(.942)
Annual Sales
Voluntariness
Indicator
EE1
EE2
EE3
EE4
PE1
PE2
PE3
PE4
FC1
FC2
FC3
FC4
FC5
FC6
SI1
SI2
DV1
DV2
DV3
Sales
Voluntary
Mean
3.610
3.670
3.520
3.630
3.920
3.670
3.650
3.780
3.380
3.440
3.310
3.270
3.540
3.570
3.360
3.490
3.230
3.330
3.100
3.400
1.220
SD
.738
.790
.765
.755
.835
.964
.943
.978
1.030
.977
.904
.971
.993
.967
.853
.826
1.114
1.150
1.198
1.280
.415
Loading
.837
.808
.857
.829
.824
.900
.868
.843
.825
.790
.803
.798
.821
.858
.941
.929
.925
.923
.912
1
1
t-value
24.705
22.191
30.288
21.119
25.005
54.735
32.963
30.602
26.422
22.155
26.592
24.114
31.201
41.429
85.138
45.083
57.791
62.180
49.307
n/a
n/a
27
Table 17 – Revised Correlations among Major Constructs
Adoption
Effort Expectancy (EE)
Facilitating Conditions (FC)
Performance Expectancy (PE)
Social Influence (SI)
Annual Sales (Sales)
Voluntariness (Voluntary)
Diagonals are AVE values.
* p < .05; **p < .01 (2-tailed)
Adoption
0.846
0.416**
0.570**
0.441**
0.437**
0.085
0.311**
EE
0.694
0.512**
0.616**
0.447**
0.060
0.106
FC
PE
SI
0.666
0.503** 0.739
0.624** 0.515** 0.874
0.233** 0.078
0.065
0.345** 0.183** 0.185**
Sales
Voluntary
1.000
0.177*
1.000
28
Table 18 - Revised Model Item Loadings and Cross Loadings
DV1
DV2
DV3
EE1
EE2
EE3
EE4
FC1
FC2
FC3
FC4
FC5
FC6
PE1
PE2
PE3
PE4
SI1
SI2
Sales
Voluntary
Adoption
0.925
0.923
0.912
0.379
0.328
0.347
0.325
0.458
0.384
0.410
0.430
0.540
0.531
0.350
0.400
0.353
0.406
0.425
0.390
0.085
0.311
EE
0.385
0.406
0.357
0.837
0.808
0.857
0.829
0.381
0.328
0.473
0.467
0.427
0.430
0.498
0.546
0.557
0.516
0.411
0.425
0.060
0.106
FC
0.528
0.515
0.529
0.500
0.352
0.467
0.373
0.825
0.790
0.803
0.798
0.821
0.858
0.405
0.451
0.419
0.449
0.612
0.553
0.233
0.344
PE
0.420
0.384
0.412
0.581
0.524
0.473
0.466
0.363
0.335
0.389
0.372
0.531
0.435
0.824
0.900
0.868
0.843
0.485
0.478
0.078
0.183
SI
0.390
0.423
0.393
0.490
0.333
0.326
0.324
0.410
0.444
0.527
0.460
0.602
0.580
0.487
0.450
0.390
0.443
0.941
0.929
0.065
0.185
Sales
0.028
0.073
0.131
0.032
0.064
0.064
0.041
0.261
0.242
0.219
0.187
0.086
0.179
0.062
0.115
0.058
0.032
0.088
0.031
1.000
0.177
voluntary
0.272
0.240
0.343
0.043
0.119
0.134
0.063
0.297
0.282
0.316
0.270
0.235
0.298
0.231
0.133
0.101
0.166
0.211
0.132
0.177
1.000
29
Figure 1 - UTAUT Model (Venkatesh et al. 2003)
Performance
Expectancy
Effort
Expectancy
Behavioral
Intention
Use
Behavior
Social
Influence
Facilitating
Conditions
Voluntariness of use
Experience
Age
Gender
Figure 2 - Research Model
Effort
Expectancy
H1
Performance
Expectancy
H2
Adoption
H3
Facilitating
Conditions
H4
Social
Influence
H5d
H6
H5c
H5b
H5a
Sales
Voluntary
30
Figure 3 - UTAUT Findings
Effort
Expectancy
.152*
Performance
Expectancy
Adoption
R2 = .443
Facilitating
Conditions
.331**
Social
Influence
-.253**
-.141**
Sales
Voluntary
31
Figure 4 - Revised UTAUT Findings
Effort
Expectancy
.143*
Performance
Expectancy
Adoption
R2 = .443
Facilitating
Conditions
Social
Influence
.387**
-.159**
-.128**
Sales
Voluntary
32