The antecedents of the use of continuous auditing in theinternal auditing context

George C. Gonzalez a,b,, Pratyusa University of Lethbridge, 615 Macleod Trail SE, Calgab Joseph M. Katz Graduate School of Business, Universi

a r t i c l e i n f o

the relationship between performance expectancy and social inuence

International Journal of Accounting Information Systems 13 (2012) 248262

Contents lists available at SciVerse ScienceDirect

International Journal of AccountingInformation Systemsrespectively. Additionally, we nd regional differences in the signi-cance of key UTAUT antecedents. Specically, we nd that the NorthAmerican internal auditors are more likely to use continuous auditingdue to soft social coercion pressures of Social Inuence through peersand higher authorities. On the other hand, Middle Eastern auditors aremore likely to use the technology if it is mandated by the higherauthorities.

2012 Elsevier Inc. All rights reserved. Corresponding author at: University of LethbridgE-mail address: george.gonzalez@uleth.ca (G.C. G

1 University of Pittsburgh, 229 Mervis Hall, Pittsbu2 University of Pittsburgh, 342 Mervis Hall, Pittsbu

1467-0895/$ see front matter 2012 Elsevier Inc.doi:10.1016/j.accinf.2012.06.009intentions to use continuous auditing. We also nd that annual salesvolume of the company and voluntariness of use signicantly moderateContinuous assuranceInternal auditingUTAUTTechnology useh N. Sharma b,1, Dennis F. Galletta b,2

ry, AB T2G 4T8, Canadaty of Pittsburgh, Roberto Clemente Drive, Pittsburgh, PA 15260, United States

a b s t r a c t

The concept of continuous auditing originated over two decades ago.Yet despite its much touted benets, its acceptance and use in practicehas been slow. To gain insight into the state of affairs, we surveyed 210internal auditors worldwide on the status of their use of continuousauditing. Using the Unied Theory of Acceptance and Use of Technology(UTAUT) we explore the antecedents of internal auditors' intentions touse continuous auditing technology. Employing the Partial LeastSquares method, we nd strong support for the model with an R2 of44.3%. Specically, we nd that internal auditors' perceptions of effortexpectancy and social inuence are signicant predictors of theirArticle history:Received 1 June 2011Accepted 1 June 2012

Keywords:Continuous auditinge, 615 Macleod Trail SE, Calgary, Canada AB T2G 4T8. Tel.: +1 403 332 4680.onzalez).rgh, PA 15260, United States.rgh, PA 15260, United States.

All rights reserved.

1. Introduction

Continuous auditing3 has been touted as offeringmany important benets to organizations. Among those

puzzling lag in the actual use of continuous auditing is the primary motivation for this study.

249G.C. Gonzalez et al. / International Journal of Accounting Information Systems 13 (2012) 248262Use of continuous auditing technology has so far been almost exclusively limited to the internal auditfunction (Chan and Vasarhelyi, 2011). Since 2005 some of the top international accounting rms have surveyedtheir clients' Chief Audit Executives (CAEs) and other top internal audit ofcers to gain an understanding of theircontinuous auditing practices (PricewaterhouseCoopers (PwC), 2006,2007; KPMG, 2010; KPMG International(KPMG), 2010; Grant Thornton, 2011). The results of these surveys vary in terms of howextensively continuousauditing was being used in practice. For example, one survey showed that of the surveying accounting rm'sclients, 13% had a continuous auditing system that was fully operational and 37% had a system in place but notyet fully developed (PwC, 2006). Another rm's survey indicated gures of 7% and 13%, respectively (KPMG,2010). A consistent theme among respondents of these surveys over the years, however, is the uniformapparent optimism of the respondents, followed each time by minimal usage of these technologies in thefollowing survey: regardless of the level to which continuous auditing had been used, the survey respondentsexpected a considerably higher level of use in two years' time. Even in the face of repeated optimism expressedby survey respondents, it appears that the implementation of continuous auditing has actually advanced veryslowly. This again raises the question of why there seems to be a lag in the use of continuous auditing.

Given the potential benets of continuous auditing, some of which were previously mentioned (also see:Debreceny et al., 2005; Flowerday and von Solms, 2005; Kogan et al., 1999; Rezaee et al., 2002; Vasarhelyi et al.,2002), this lag is puzzling.We thus set out to gain insight into this state of affairs by conducting an online surveyof industry practitioners' internal audit practices and analyzing their responses through the lens of the UniedTheory of Acceptance and Use of Technology (UTAUT) from the Management Information Systems (MIS)discipline (Venkatesh et al., 2003). UTAUT provides a theoretical framework upon which to assess the use of aparticular type of technology. In this study, we nd that the UTAUT model explains a substantial amount ofvariance in the intentions to use continuous auditing. Most importantly, we nd that perceptions of effortexpectancy and social inuence are signicant predictors of internal auditors' intentions to use, whileperformance expectancy and facilitating conditions are not. Annual sales volume of the company and voluntarinessof use signicantly moderate the relationship between performance expectancy and social inuence respectively.

Our paper is organized as follows. In the next section we briey present an introduction to the UTAUTframework. Subsequent sections present our research model and hypotheses, discuss our data collectionmethod, and outline the data analysis procedure and the results of model testing. Finally we conclude witha discussion of our ndings and implications for practice.

2. Theoretical framework

ManyMIS researchers have studied acceptance of new technologies over the past twodecades. The Theory ofReasoned Action (TRA) (Fishbein and Ajzen, 1975) and the Theory of Planned Behavior (TPB) (Ajzen, 1985,1987, 1991) have greatly informed work in determining behavioral intentions. TRA states that antecedents ofbehavioral intentions are attitudes and subjective norms. TPB added perceived behavioral control to the twoantecedents of TRA and also added a direct relationship between perceived behavioral control and actual

3 The focus of a particular continuous auditing technique can range from controls-based (continuous controls assessment) torisk-based (continuous risk assessment) (IIA, 2005). Our use of the term continuous auditing is intended to encompass anytechniques along this spectrum.4benets are the minimization of accounting errors, more timely analysis and organizational communication,and increased audit efciency and effectiveness. Various research studies have explained the benets ofcontinuous auditing (Vasarhelyi et al., 2004; Kuhn and Sutton, 2006), discussed technical aspects ofimplementing continuous auditing technology (Kuhn and Sutton, 2010), explored actual implementations inpractice (Hermanson et al., 2006), and examined the psychological effects of continuous auditing onmanagers(Hunton et al., 2008, 2010).4 Yet while the concept of continuous auditing, rst introduced by Groomer andMurthy (1989) and Vasarhelyi and Halper (1991), is about two decades old, the actual practice of continuousauditing has remained the exception rather than the rule (Alles et al., 2008; Chan and Vasarhelyi, 2011). ThisFor a review of the literature see Brown et al. (2007).

behavior. In her study of external audit support system use, Dowling (2009) develops a model by combiningprinciples of TPB and Adaptive Structuration Theory (DeSanctis and Poole, 1994) to investigate external auditoruse of rm audit support systems.

Davis (1989) adapted TRA and TPB to theMIS literature to create thewidely-cited Technology AcceptanceModel (TAM) (Davis, 1989), which had dramatic inuence on the MIS eld. TAM, which focuses ontechnology acceptance, provides usefulness and ease of use as antecedents to behavioral intentions. Otherversions of TAM have made slight changes to the constructs over the years, but the basic principles haveremained the same. A few technology acceptance studies in the accounting literature have used TAM. Oneexamined the effects of training on user acceptance of electronic work papers (Bedard et al., 2003). Anotherstudy explored differing attitudes towards technology concluded that social studies students perceivedtechnology to be less useful and not as easy to use as did business students (Greeneld and Rohde, 2009). Athird study, more closely related to the current one, examined how groups of features of Generalized AuditSoftware (GAS) affect internal auditors' technology acceptance behavior, nding that perceived usefulness

250 G.C. Gonzalez et al. / International Journal of Accounting Information Systems 13 (2012) 248262hasmore impact on the usage of basic GAS features than perceived ease of use and, conversely, perceived easeof use has more impact on the usage of advanced GAS features than perceived usefulness (Kim et al., 2009).

After over a decade of research on TAM, Davis and colleagues proposed the Unied Theory of Acceptanceand Use of Technology (UTAUT) (Venkatesh et al., 2003), which has taken the place of the various TAMmodels (see Fig. 1). UTAUT has been used in many contexts to predict behavioral intentions. It served as themodel for examining factors affecting an in-charge external auditor's decision to implement new technologyon an engagement (Curtis and Payne, 2008; Dowling, 2008). The basic notion underlying UTAUT is that threeantecedentswill predict behavioral intentions: performance expectancy (formerly perceived usefulness), effortexpectancy (formerly perceived ease-of-use), and social inuence (not in the original TAM model). A directantecedent of actual behavior is facilitating conditions. Finally, control variables moderate the relationships ofthe four antecedents of intentions: gender, age, experience, and voluntariness of use.

TAM and UTAUT have been shown to enrich our understanding of computer-related use behaviors inmany contexts. UTAUT is therefore employed in this study to understand use of continuous auditing tools. Ifthere is a shortfall in use of such tools, it is important to determine to what extent the various antecedentsplay a role. For instance, if the problem is the lack of positive perceptions of effort expectancy among internalauditors, training programs might be useful to reduce the usability barriers. Alternatively, system designersmay need to developmore user friendly continuous auditing systems that auditors would prefer to use. If, onthe other hand, the problem is the lack of positive perceptions of performance expectancy, demonstrations andperformance statistics could be useful. Therefore, understanding any lack of positive perceptions amonginternal auditors concerning the key UTAUT antecedents should allow the rms to take concrete steps inmanaging these perceptions and hence encouraging the use of continuous auditing technology.

Since the TAM and UTAUT models were proposed to explain individual level use of technology, it isreasonable to assume that the intentions of individuals embedded within organizations to use continuousauditing technologies also depend on most of the same constructs. Executives would need to be cognizantof perceptions of effort expectancy as well as perceptions of performance expectancy in deciding toencourage the use of the technology. Facilitating infrastructure needs to be in place as well. Further,

Performance Expectancy

Effort Expectancy

Social Influence

Facilitating Conditions

Behavioral Intention

Voluntariness of use Experience

Use Behavior

AgeGenderFig. 1. UTAUT model (Venkatesh et al., 2003).

internal auditors even have social pressures from both within and outside of their rms. Internal pressurepoints would include those peers and supervisors, as UTAUT species. External pressures are likely tomake this variable even more important in the case of continuous auditing; the widespread use of thesetechnologies by other rms, and the promotion of the technology by inuential professional groups suchas the Institute of Internal Auditors (IIA) and the Institute of Management Accountants (IMA), can benaturally considered to be a benchmark that they should follow so they do not fall behind. Not followingemerging best practices could even create exposure for a rm, such as the failure to detect fraud as a resultof not implementing advanced technologies such as continuous auditing.

Whilewe select the key antecedents in ourmodel based onUTAUT,we believe that the setting of our studyrequires a unique set ofmoderating and control variables. Becausewe focus on internal auditors embedded inrms,we need somemechanism to account for differences among therms thatmay affect the perceptions ofinternal auditors regarding the key UTAUT antecedents and their effect on intentions to use the technology.For example, rms with higher sales may have access to better infrastructure and/or training mechanisms topromote the use of continuous auditing among their internal auditors. Therefore we chose annual sales as amoderator variable in our model to account for the rms' abilities to acquire the technology and promote its

251G.C. Gonzalez et al. / International Journal of Accounting Information Systems 13 (2012) 248262use among their employees. We also use a voluntariness to use as a moderator variable that is identical to theone in UTAUT, because it accounts directly for situations in which the use of such technologies is dictated byanother party.

3. Research model and hypotheses

As per the discussion above, we expect six constructs to play a signicant role in internal auditors'intentions to use continuous auditing technology: effort expectancy, performance expectancy, facilitatingconditions, social inuence, voluntariness of use and annual sales of the company. In the remainder of thissection, we dene each of the determinants, specify the role of key moderators (voluntariness and annualsales), and provide the theoretical justication for the hypotheses. Fig. 2 presents our research model.

3.1. Effort expectancy

Effort expectancy is dened 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 oftheirwork responsibilities froma traditionally reactive approach to a proactive approach (Chan and Vasarhelyi,2011). Audit procedures used for transaction and compliance verication are automated in the continuousauditing environment. The automation of transaction and compliance audit procedures shift the auditor's workto more complex audit objectives, such as dealing with verications of estimates, adherence to standards, andother items that require auditor judgment. Hence, the auditor's main role in the present continuous auditingenvironment involves investigating irregularities/exceptions identied by the continuous auditing system and

Effort Expectancy

Performance Expectancy

Facilitating Conditions

Social Influence

Intention to Use

VoluntarySales

H1

H4

H5c

H6

H2

H5bH5a

H5d

H3Fig. 2. Research model.

dealing with audit procedures requiring judgment (Chan and Vasarhelyi, 2011). The more seamlessly andeffortlessly the auditors are able to transition into using the continuous auditing systems in this new role, thehigher will be their intentions to use them. Additionally, continuous auditing systems employ (or will employ)enabling technologies including statistical methodologies such as belief functions and neural networks, as wellas technologies from computer science such as database and expert systems, intelligent agents, andtechnologies for tagging data to facilitate transmission and comparison, most notably XBRL and XBRLGL(Vasarhelyi et al., 2004). Internal auditors will not only have to overcome the learning curve to become skillfulat using continuous auditing systems, but also to nd them easy and efcient to use. Efcient use is especiallyrelevant because auditors will be interacting frequently with the continuous auditing system. Given thecomplexities involved, the use of continuous auditing systems will therefore be facilitated by the positiveperceptions among internal auditors with regard to effort expectancy. Therefore we hypothesize:

H1. Positive perceptions of effort expectancy will increase internal auditors' intentions to use continuousauditing technology.

3.2. Performance expectancy

Performance expectancy is dened as the degree to which an individual believes that using a system willhelp achieve gains in job performance (Venkatesh et al., 2003). Continuous auditing is likely to have manybenets such as continuous error and fraud detection, and the use of data analytics and data modelingfeatures (Vasarhelyi et al., 2004), all of which lead to an enhanced internal control system. Given thesebenets, use of continuous auditing will be facilitated by internal auditors' perceptions of usefulness of thesystem in their work and the productivity gains they can expect from it. Therefore, we argue that to the extentthat internal auditors perceive continuous auditing as being better than using its precursor, traditionalperiodic auditing, they are likely to have positive intentions to use continuous auditing technology.

H2. Positive perceptions of performance expectancy will increase internal auditors' intentions to usecontinuous auditing technology.

3.3. Facilitating conditions

Facilitating conditions are dened as the degree to which an individual believes that an organizationaland technical infrastructure exists to support use of the system. These conditions include aspects of thetechnological and/or organizational environment that are designed to remove barriers to the use of asystem (Venkatesh et al., 2003). This relates to technical, monetary and training support and the resourcesavailable to the internal auditors in facilitating their use of the continuous auditing system. As mentionedearlier, the use of continuous auditing systems is likely to involve learning of enabling technologies andinternal auditors who either possess the background knowledge or have access to the resources requiredto learn will have more positive perceptions of facilitating conditions. The continuous auditing systems alsohave to be compatible with systems auditors are already using.

In the original UTAUT model, facilitating conditions are hypothesized only to impact actual use, notintentions. In studies that do not examine actual behavior, it is perhaps worthwhile to include facilitatingconditions at least for exploratory purposes. In our study, including facilitating conditions is not purelyexploratory, as the users in this sample are professionals and aremore likely to be at least somewhat aware ofthose conditions in advance than clerical staff employees. Therefore, in our next hypothesis we propose:

H3. Positive perceptions of facilitating conditions will increase internal auditors' intentions to usecontinuous auditing technology.

3.4. Social inuence

Social inuence is dened as the degree to which an individual perceives that people important to him orher believe he or she should use the system. Social inuence as a direct determinant of behavioral intention is

252 G.C. Gonzalez et al. / International Journal of Accounting Information Systems 13 (2012) 248262represented as subjective norm, i.e., the explicit or implicit notion that the individual's behavior is inuenced

by theway inwhich they believe otherswill view themas a result of havingused the technology (Venkatesh etal., 2003). Internal auditors' perception of social inuence originates from their peers and from their superiorsin the highermanagement. It is up to the internal auditors' superiors to, among other things, commit to the useof continuous auditing technology, obtain agreement from all affected inuential parties, and secure the

253G.C. Gonzalez et al. / International Journal of Accounting Information Systems 13 (2012) 248262organizational approval of funds needed to implement the technology. We postulate in this hypothesis that:

H4. Positive perceptions of social inuence will increase internal auditors' intentions to use continuousauditing technology.

3.5. Moderating variables

UTAUT calls for several moderators that are dependent on the particular adopter of the technology. Becausethis is not personal technology, we adapted the use of the model by replacing Gender, Age and Experience withAnnual Sales. Annual sales is likely to be a much better moderator variable, given that the gender, age andexperience of the potential user embedded in an organization are less likely to impact intentions to use thetechnology. Therefore, the annual sales of the organization is considered to be a more appropriate moderatingfactor. As mentioned previously, organizations with higher sales may have access to better infrastructure and/ortrainingmechanisms topromote theuse of continuous auditing among their internal auditors For instance, if effortexpectancy (usability) or performance expectancy (usefulness) is considered to be a potential problem, organi-zations 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 onlyfor certain paths. UTAUT predicts that for performance expectancy, effort expectancy, social inuence, andfacilitating conditions, gender only moderates the rst three, age moderates all, and experience moderatesonly the last three on behavioral intentions. Because we replaced gender, age, and experience with annualsales, we hypothesize that annual sales will moderate all of those relationships.

H5a. Annual sales will moderate the relationship between effort expectancy and internal auditors'intentions to use the continuous auditing technology.

H5b. Annual sales will moderate the relationship between performance expectancy and internalauditors' intentions to use the continuous auditing technology.

H5c. Annual sales will moderate the relationship between facilitating conditions and internal auditors'intentions to use the continuous auditing technology.

H5d. Annual sales will moderate the relationship between social inuence and internal auditors'intentions to use the continuous auditing technology.

The other moderating variable in UTAUT and our model is voluntariness of use. According to the UTAUTmodel as depicted in Fig. 1, voluntariness of use only moderates the relationship between social inuenceand behavioral intention. We maintain that aspect of UTAUT in our research model. Therefore:

H6. Voluntariness of use will moderate the relationship between social inuence and intention to usethe continuous auditing technology.

4. Data and sample

We conducted an online survey by e-mailing respondents a link to our electronic survey site.5 The surveyfor this studywas e-mailed on our behalf by the IMA to theirworldwidememberswhosemembership prolelisted one of the following responsibilities: internal auditing, risk management, information systems orgeneral accounting.6 The survey e-mail explained the nature of the survey and asked respondents to complete

5 The Qualtrics tool was used for this survey.6 The IMA has substantially more members who prole 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 capturedpractitioners 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.

the survey provided they were knowledgeable on their company's continuous auditing efforts and, if not, toforward the e-mail to someone within their company's internal audit function.

The number of surveys e-mailed was 9013 and the number of usable responses was 210 (a response

254 G.C. Gonzalez et al. / International Journal of Accounting Information Systems 13 (2012) 248262rate of 2.33%). Of those 210 usable responses, the percentage breakdown by regional geographic locationof company operations was: North America 59.0%; Middle East 28.6%; Asia 5.7%; Europe 4.8%; and others1.9%. These percentages are comparable to the IMA's worldwide membership breakdown of NorthAmerica 72%; Middle East 16%; Asia 8%; Europe 3%; and others 1%.7

The rst question in the survey posed to respondents was What is the current state of ContinuousAuditing in your company? The four possible responses and the percentage of respondents selecting eachresponse were:

Fully operational in one or more of our company's systems 21% In place but not yet fully developed 22% Not implemented yet but scheduled to be implemented in future 16% Not implemented and no plans for future implementation 40%.

After the introductory question, respondents were asked to answer a set of approximately 40questions. The questions were of three main types: (1) company prole questions such as annual sales andgeographic location of operations, (2) questions regarding current use of and future plans for continuousauditing, and (3) questions structured on the UTAUT technology use framework described in the lastsection. The actual survey questions for this latter group are shown in Table 1.8 Survey responses to thesequestions enabled us to analyze the current state of use of continuous auditing technology using theUTAUT framework. We next describe the method and analysis.

5. Research methods

To estimate the paths between the constructs shown in our research model (Fig. 2), and thereby testthe propositions advanced previously, we used partial least squares (PLS) analysis, which is a powerfulmultivariate analysis technique. PLS is useful for analyzing structural equations with latent variables. It issimilar to LISREL, which is probably the best known of the second generation statistical techniques, in thesense 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 (Compeauand Higgins, 1995). PLS is most appropriate when sample sizes are small, when assumptions ofmultivariate normality and interval scaled data cannot be made, and/or when the researcher is primarilyconcerned with prediction of the dependent variable (Birkinshaw et al., 1995). The major benets of PLSinclude robustness for small to medium sample sizes and fewer constraints on the data (e.g., normalityassumptions) compared to covariance-based methods such as LISREL (Wakeeld et al., 2008). Simulationstudies have also shown PLS to be robust against inadequacies such as multi-collinearity, skewness andomission of regressors (Cassel et al., 1999).

In PLS all relationships are modeled simultaneously, sharply reducing concerns about multicollinearity(Inkpen and Birkinshaw, 1994). The path coefcients obtained from a PLS analysis are standardizedregression coefcients, while the loadings of items on individual constructs are factor loadings. Factor scorescreated using these loadings are equivalent to weighted composite indices. Thus, PLS results can be easilyinterpreted by considering them in the context of regression and factor analysis (Birkinshaw et al., 1995). TheR2 values are used to assess the proportion of variance in the endogenous constructs which can be accountedfor by the antecedent constructs (Compeau and Higgins, 1995). Generally, PLS results are presented in twostages. In the rst stage, the researcher ensures that the measures used as operationalizations of theunderlying constructs are both reliable and valid. Once convinced of the adequacy of themeasurementmodel,the researcher proceeds to the second stage and interprets the resultingmodel coefcients (Birkinshaw et al.,1995).

7 IMA membership prole information as of April 30, 2011.8 The actual survey questions for the rst two of the three referenced question types were included in the survey for descriptive

data gathering purposes only. Their response data are not used in our theoretical framework and analysis and have therefore not

been included in this paper. They are available upon request.

Table 1Constructs and corresponding survey questions based on UTAUT framework.

Construct Indicator Survey questiona

Effort expectancy EE1 Interacting with the continuous auditing system is/would be generally clear and understandableEE2 It is/would be generally easy to become skillful at using the continuous auditing systemEE3 I (we) nd/would nd the continuous auditing system easy to useEE4 Learning to operate the continuous auditing system is/would be easy for me (us)

Performanceexpectancy

PE1 A continuous auditing system is/would be useful in my (our) jobPE2 A continuous auditing system enables/would enable me (us) to accomplish tasks more quicklyPE3 A continuous auditing system increases/would increase my (our) productivityPE4 A continuous auditing system increases/would increase my (our) chances of improving my

(our) nancial positionFacilitatingconditions

FC1 My (our) company has the resources necessary to use the continuous auditing systemFC2 My (our) company has the knowledge necessary to use the continuous auditing systemFC3 The continuous auditing system is/would be compatible with other systems I (we) useFC4 A specic person (or group) is/would be available for assistance with the continuous auditing

system difcultiesSocial inuence SI1 People or parties who inuence my (our) behavior think/would think that I (we) should use the

255G.C. Gonzalez et al. / International Journal of Accounting Information Systems 13 (2012) 2482626. Data analysis and results

6.1. Measurement model

Convergent validity indicates that measures of constructs that should be theoretically related to eachother are, in fact, observed to be related. A composite reliability value of 0.70 or above and an averagevariance extracted value of more than 0.50 are deemed as indicators of acceptable level of convergentvalidity of measures (Chin, 1998). As evident in Table 2, all average variance extracted (AVE) values areabove .50 and composite reliability coefcients are above .70 for each construct. This indicates that themeasurements are reliable and the latent constructs account for more than 50% of the variance in theitems. The loadings are also in the acceptable range and all the t-values shown in the table suggest thatthey are signicant at the .01 level.

Discriminant validity is the extent to which the measure is not a reection of some other construct. It isindicated by low correlations between the measure of interest and other measures. If the square root ofthe AVE is greater than all of the inter-construct correlations, it is evidence of sufcient discriminantvalidity (Chin, 1998). Table 3 suggests that our measurement model demonstrates sufcient discriminantvalidity.

continuous auditing systemSI2 People or parties who are important to me (us) think/would think that I (we) should use the

continuous auditing systemSI3 Senior management has been/would be helpful in the use of the continuous auditing systemSI4 In general, my (our) organization has supported/would support the use of the continuous

auditing systemIntention to use 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 futureDV3 My (our) use of continuous auditing is very likely to occur soon

Annual sales Sales 1 = less than $1 million; 2 = $1 million to $10 million; 3 = $10 million to $100 million;4 = $100 million to $1 billion; 5 = more than $1 billion

Voluntariness Voluntary The use of continuous auditing system/if in the future your company were to adopt thecontinuous auditing system, its use is likely to be: 1 = voluntary; 2 = mandated

a These questions represent the set of questions we asked of survey respondents, as designated by UTAUT. Prior to asking theabove set of UTAUT questions, we identied whether respondents (A) currently have a continuous auditing system, either fullyoperational or in place but not yet fully implemented, or (B) had a scheduled but not yet implemented continuous auditing system,or did not have any plans for a system. The above UTAUT questions were worded identically for all respondents, except that for Acategory respondents the questions were worded in the present tense (is, etc.), while for B category respondents the questionswere worded in the future tense (would be, etc.). This minor distinction is reected above by including both sets of words (e.g., is/would be). For purposes of clarity in the survey, however, actual survey questions included usage of one or the other of the twotenses, but not both as shown above. For each but the last two questions above, respondents had ve choices from which to choose(1 = strongly disagree; 5 = strongly agree). For the other two questions (annual sales and voluntariness) respondents' choices wereas shown in the survey question column.

Table 2Loadings of the indicator variable.

Constructa Indicator Mean SD Loading t-value

Effort expectancy (.694) (.900) EE1 3.610 .738 .837 24.208EE2 3.670 .790 .808 19.847EE3 3.520 .765 .857 29.844EE4 3.630 .755 .829 20.705

Performance expectancy (.739) (.918) PE1 3.920 .835 .824 25.568PE2 3.670 .964 .900 51.707PE3 3.650 .943 .868 31.637PE4 3.780 .978 .843 30.594

Facilitating conditions (.713) (.908) FC1 3.380 1.030 .867 36.932FC2 3.440 .977 .844 29.453FC3 3.310 .904 .833 31.714

256 G.C. Gonzalez et al. / International Journal of Accounting Information Systems 13 (2012) 248262To further assess validity of our measurements, we also constructed a cross loading table (Table 4) assuggested by Gefen et al. (2000). If each item loading in the table is higher on its assigned construct thanon other constructs, it is evidence of adequate convergent and discriminant validity. As can be seen inTable 4, all the diagonal elements are high and also greater than off-diagonal elements, suggesting

FC4 3.270 .971 .834 28.378Social inuence (.710) (.907) SI1 3.360 .853 .828 26.095

SI2 3.490 .826 .808 18.502SI3 3.540 .993 .872 47.838SI4 3.570 .967 .860 34.815

Intention to use (.846) (.942) DV1 3.230 1.114 .925 56.959DV2 3.330 1.150 .924 64.594DV3 3.100 1.198 .911 49.480

Annual sales Sales 3.400 1.280 1 n/aVoluntariness Voluntary 1.220 .415 1 n/a

a The gures in parentheses shown underneath each construct name are average variance extracted (AVE) and compositereliability, respectively.adequate convergent and discriminant validity of our measures.

6.2. Common method bias

Common method bias 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 correlationsbetween measures. Thus, at least partially, common method biases may pose a rival explanation for theobserved correlations between the measures. Similarly, a common rater bias may occur due to anyartifactual covariance between predictor and the criterion variable produced by the fact that therespondent providing the measure of these variables is the same (Podsakoff et al., 2003). In order to ruleout any rival explanations due to common method bias, we follow Liang et al. (2007) and include in the

Table 3Correlations among major constructs.

Intention to use EE FC PE SI Sales Voluntary

Intention to use 0.846Effort expectancy (EE) 0.416 0.694Facilitating conditions (FC) 0.499 0.489 0.713Performance expectancy (PE) 0.441 0.616 0.433 0.739Social inuence (SI) 0.568 0.501 0.713 0.571 0.710Annual sales (sales) 0.085 0.060 0.269 0.078 0.120 1.000Voluntariness (voluntary) 0.311 0.106 0.345 0.183 0.267 0.177 1.000

Diagonals are AVE values. pb .05 (2-tailed).

pb .01 (2-tailed).

PLS model a common factor whose indicators include all the principal constructs' indicators and calculateeach indicators' variances substantively explained by the principal construct and by the method.

Table 4Item loadings and cross loadings.

Intention to use EE FC PE SI Sales Voluntary

DV1 0.925 0.385 0.459 0.420 0.523 0.028 0.272DV2 0.924 0.406 0.440 0.384 0.537 0.073 0.240DV3 0.911 0.357 0.478 0.412 0.509 0.131 0.343EE1 0.379 0.837 0.457 0.581 0.533 0.032 0.043EE2 0.328 0.808 0.337 0.524 0.357 0.064 0.119EE3 0.347 0.857 0.456 0.473 0.410 0.064 0.134EE4 0.325 0.829 0.369 0.466 0.353 0.041 0.063FC1 0.458 0.381 0.867 0.363 0.589 0.261 0.297FC2 0.384 0.328 0.844 0.335 0.575 0.242 0.282FC3 0.409 0.473 0.833 0.389 0.644 0.219 0.316FC4 0.430 0.467 0.834 0.372 0.603 0.187 0.270PE1 0.350 0.498 0.356 0.824 0.487 0.062 0.231PE2 0.400 0.546 0.390 0.900 0.505 0.115 0.133PE3 0.353 0.557 0.350 0.868 0.466 0.058 0.101PE4 0.406 0.516 0.387 0.843 0.501 0.032 0.166SI1 0.425 0.411 0.544 0.485 0.828 0.088 0.211SI2 0.390 0.425 0.468 0.478 0.808 0.031 0.132SI3 0.540 0.427 0.645 0.531 0.872 0.086 0.235SI4 0.531 0.430 0.706 0.435 0.860 0.179 0.298Sales 0.085 0.060 0.269 0.078 0.120 1.000 0.177Voluntary 0.311 0.106 0.345 0.183 0.267 0.177 1.000

257G.C. Gonzalez et al. / International Journal of Accounting Information Systems 13 (2012) 248262The evidence for common method bias can be obtained by examining the statistical signicance of thefactor loadings on the method factor and comparing the variances of each observed indicator explained byits substantive construct and the method factor (Williams et al., 2003). The squared values of substantive

Table 5Common method bias analysis.Construct Indicators Substantive factor loading (R1) R12 Method factor loading (R2) R22

Effort expectancy EE1 0.671*** 0.450 0.198** 0.039EE2 0.840*** 0.706 0.045 0.002EE3 0.893*** 0.797 0.037 0.001EE4 0.926*** 0.857 0.114* 0.013

Performance expectancy PE1 0.825*** 0.681 0.002 0.000PE2 0.912*** 0.832 0.012 0.000PE3 0.916*** 0.839 0.052 0.003PE4 0.780*** 0.608 0.066 0.004

Facilitating conditions FC1 0.895*** 0.801 0.042 0.002FC2 0.966*** 0.933 0.14** 0.020FC3 0.748*** 0.560 0.109* 0.012FC4 0.767*** 0.588 0.078 0.006

Social inuence SI1 0.951*** 0.904 0.117 0.014SI2 0.995*** 0.990 0.185** 0.034SI3 0.721*** 0.520 0.152* 0.023SI4 0.715*** 0.511 0.143* 0.020

Voluntariness Voluntary 1.000*** 1.000 0.000 0.000Annual sales Sales 1.000*** 1.000 0.000 0.000Intention to use DV1 0.926*** 0.857 0.001 0.000

DV2 0.932*** 0.869 0.008 0.000DV3 0.902*** 0.814 0.008 0.000

Average 0.768 0.000 0.009

pb .05. pb .01.

pb .005.

Effort

258 G.C. Gonzalez et al. / International Journal of Accounting Information Systems 13 (2012) 248262and method factor loadings are interpreted as variances explained by the substantive and methodconstructs, respectively. If the method factor loadings are insignicant and substantive variances aresubstantially greater than the method variances, then we may conclude that common method bias isunlikely to be of any concern (Liang et al., 2007). As seen in Table 5, the average substantive variance is.768 while the average method variance is .009. The ratio of substantive variance to method variance isthus about 85:1. This ratio suggests that the variance explained by the model is 85 times more than thevariance attributable to commonmethod bias. In addition, most method factor loadings are not signicant.Given the small magnitude and insignicance of method variance we argue that common method bias isunlikely to be of concern for this study.9

6.3. Hypothesis testing

Expectancy

Performance Expectancy

Facilitating Conditions

Social Influence

Intentionto Use

R2 = .443

VoluntarySales

.152*

.331**

.141**.253**

* p < .05 ; ** p < .01 Note: Solid lines in the figure represent significant paths while insignificant pathsare represented by dashed lines.

-

-

Fig. 3. UTAUT ndings. *pb .05 and **pb .01. Note: solid lines in the gure represent signicant paths while insignicant paths arerepresented by dashed lines.Fig. 3 presents the estimates obtained from the PLS analysis. The R2 value of .443 indicates that asignicant amount of variance (44.3%) is explained by the model. The effect of effort expectancy onintention to use was signicant at the .05 level (b=.152), providing support for H1. Our analysis did notnd support for H2 or H3 which represent the effects of performance expectancy and facilitating conditionson intention to use, respectively. Social inuence was found to have a signicant impact on internalauditors' intentions to use continuous auditing technology (b=.331, pb .01) providing support for H4.

As for H5aH5d, represented by the four arrows extending from the Sales box in Fig. 3, the gure showsthat the links for H5a, H5b and H5d are insignicant thereby failing to conrm the moderating role ofannual sales on effort expectancy, facilitating conditions and social inuence, respectively. However, the linkfor H5c, performance expectancy, was signicant at the .01 level (b=.141). Finally, the link for H6 wassignicant at the .01 level (b=.253) thereby lending support to the role of voluntariness of use as amoderator for the relationship between social inuence and intention to use.

6.4. Additional analysis

To further analyze our worldwide data set and to explore whether respondents coming from differentparts of the world may be responding differently, we split our data based on the two largest demographicrespondent groups in our sample. With 124 responses, North America was our largest demographic

9 To our knowledge there are no rules of thumb to suggest how large this ratio should be. However, the magnitude of the 85:1ratio reported in this study compares favorably to the 42:1 ratio reported by Liang et al. (2007).

respondent group, followed by 60 respondents from the Middle East. The rest of the 26 respondents camefrom Europe, Asia, Australasia and South America combined (labeled as Other Regions). Using these 3respondent groups, we analyzed a main-effects only model with no interaction effects because we deemedthe sample sizes of each demographic group to be insufcient for robust moderation analyses. Table 6presents the region-wise breakdown of the results.

Looking at Table 6 we nd that the North America sample is indeed different from the Middle Eastsample. Social Inuencewas found to be highly signicant for the North American sample at .01 level (b=.425). In contrast, we found that Voluntariness of Use construct was highly signicant for the Middle Eastsample at the .01 level (b=.244). Since we measured voluntariness using a binary scale item, the positivepath coefcient suggests that it is the lack of voluntariness in use (i.e. mandated use) that makes it morelikely for Middle Eastern auditors to use the system.

Quiet surprisingly we also found that, given its small sample size (n=26) the Other Regionsmodel hada very high R-squared value including highly signicant paths for Performance Expectancy, Social Inuence andVoluntariness of Use. Including this sample with either the North American or Middle East samples is likely tobias results upwards in these two models as is evident in second and the fourth columns of Table 6. Onextrapolating the region wise trends from Table 6, we argue that the critical antecedent for building thepositive intentions to use Continuous Auditing technology among North American users is Social Inuence,while Voluntariness of Use (i.e. mandated use) is the critical factor for Middle Eastern users.

We believe that these interesting results can be explained using Hofstede's power distance concept

Table 6Region-wise breakdown of results.

North Americaonly

North Americaand other regions

Middle East only Middle East andother regions

Other regions

Sample size 124 150 60 86 26R-squared 41 42.1 26.5 34.3 67

Path t-val Path t-val Path t-val Path t-val Path t-valEE 0.067 0.628 0.051 0.543 0.155 0.925 0.179 1.45 0.114 1.423PE 0.053 0.491 0.025 0.281 0.16 1.45 0.202 2.158 0.21 3.252FC 0.166 1.378 0.091 0.852 0.243 1.345 0.13 0.884 0.046 0.582SI 0.425 3.85 0.47 5.261 0.013 0.089 0.164 1.65 0.579 5.565Sales 0.033 0.482 0.039 0.648 0.087 0.633 0.036 0.357 0.086 1.29Voluntariness 0.124 1.49 0.117 1.717 0.244 2.011 0.255 3.052 0.248 3.912

pb .05. pb .01.

pb .005.

259G.C. Gonzalez et al. / International Journal of Accounting Information Systems 13 (2012) 248262(Hofstede, 1980). Power distance is the extent to which the less powerful members of organizations andinstitutions accept and expect that power is distributed unequally. High power distance cultures such asthe Middle East are more likely to believe that superiors should have greater degree of power oversubordinates. In such cultures, subordinates have a higher tendency to defer to power which we believeexplains the signicance of Voluntariness of Use construct in determining the respondents' intentions touse the system. Therefore we argue that the Middle Eastern respondents are more likely to use the systemif it is mandated by higher authorities. On the other hand, cultures such as North America that believe inrelatively low power distance, hold that authorities should wield lesser degree of power oversubordinates. Respondents in such cultures are more likely to respond to democratic and consultativepressures such as Social Inuence from peers and higher management to use the system. This suggests thatcultural differences may dictate different strategies in convincing respondents to use the ContinuousAuditing system depending on their geographic location.

7. Discussion and conclusions

In spite of the fact that the concept of continuous auditing was rst introduced over two decades ago,and that the concept has garnered a considerable amount of attention in both the academic andprofessional literature, to date continuous auditing has been used to a limited extent, and almost

exclusively in the internal audit domain. While past surveys of CAEs and other internal audit executiveshave indicated that plans for the implementation of continuous auditing were robust (PwC, 2006; KPMG,2010; Grant Thornton, 2011), the results of this study's survey indicate that while some progress has beenmade, continuous auditing systems are not yet used widely.

This study explores that lack of widespread use of continuous auditing systems, and seeks answers towhy

In addition, the level of annual sales volume seems tomoderate another important construct, performance

260 G.C. Gonzalez et al. / International Journal of Accounting Information Systems 13 (2012) 248262expectancy. Large rms tend to use continuous systems without overly strict questioning of the systems'performance expectancy, but use in rms with lower sales volume tends to follow a stricter requirement thatthe software be deemed to pass cost/benet scrutiny. Another moderator, the extent to which the system isperceived to be voluntary or mandatory, also moderates the effect of social inuence on intention to use, asUTAUT predicts, which is not too surprising as the mandatory use of continuous auditing, possibly by agoverning authority10 or perhaps even by the company's board of directors, would logically carry heavierpressure than in a voluntary environment.

The UTAUT results regarding Effort Expectancy, as just discussed, seem quite positive and bode well forfuture use and expanded utilization of continuous auditing technology. It therefore becomes a matter ofcompanies' commitment to the use of the technology: an organization that decides to implement continuousauditing, or expand its use, will likely be able to count on little resistance and,more likely, strong support fromits internal auditors. Indeed, the result regarding Social Inuence bears this out it almost seems as if internalauditors are ready and waiting for their company's top management to commit to continuous auditing.

Finally, our analysis suggests strong cultural differences among the critical antecedents affectinginternal auditors' intentions to use the system. North American internal auditors are more likely torespond to soft coercion pressures created by positive Social Inuence through peers and higherauthorities. On the other hand, Middle Eastern internal auditors are much more likely to use the system ifit is mandated by the higher authorities. Therefore, we suggest that different strategies depending on thegeographic location should be employed to help create the relevant kinds of pressures on internal auditorsto use the Continuous Auditing system. The prominence of Social Inuence and Voluntariness of Useconcepts in our analysis suggests that the lag in the use of Continuous Auditing systems can be readilyexplained by the lack of socially conducive or coercive pressures in the auditing eld.

Acknowledgments

The authors thank Mary Curtis and Ray Henrickson for their valuable comments and suggestions, asdiscussants of an earlier version of this paper presented at the 7th biennial University of Waterloosymposium on information integrity and information systems assurance (held on October 2021, 2011).

Editor's Note: As Ray Henrickson's comments and suggestions have been incorporated in the presentversion of the paper, a separate set of these comments is not provided in this issue.

We are grateful to the Institute of Management Accountants for their generous support.

10 In our view the mandatory imposition of the use of continuous auditing could eventually arise from the required issuance ofXBRL-based nancial statements on frequent basis, perhaps even real-time at some point in the future. Alternatively, the impositionof continuous auditing itself may not be mandatory but would be deemed all but necessary with mandatory XBRL-based nancialthis is the case. We surveyed internal auditors worldwide about the current state of use of continuousauditing technology in their companies. Through application of the Unied Theory of Acceptance and Use ofTechnology (UTAUT) framework we analyzed respondents' answers to nd that the signicant factorsleading to intentions to use of continuous auditing technology are Effort Expectancy, i.e., the ease and clarity ofuse, and Social Inuence, i.e., the support and encouragement of key organizational members. These two keyresults suggest that two key drivers for increasing the use of continuous auditing systems would be thedemonstration to internal auditors that a continuous auditing system is easy to learn and operate and reducesthe amount of work necessary to carry out audits (Effort Expectancy), and management visibly and activelypromoting the use of continuous auditing as a valuable internal auditing tool (Social Inuence).statements.

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262 G.C. Gonzalez et al. / International Journal of Accounting Information Systems 13 (2012) 248262

The antecedents of the use of continuous auditing in the internal auditing context1. Introduction2. Theoretical framework3. Research model and hypotheses3.1. Effort expectancy3.2. Performance expectancy3.3. Facilitating conditions3.4. Social influence3.5. Moderating variables

4. Data and sample5. Research methods6. Data analysis and results6.1. Measurement model6.2. Common method bias6.3. Hypothesis testing6.4. Additional analysis

7. Discussion and conclusionsAcknowledgmentsReferences