1. TABLE OF CONTENT
TABLE OF CONTENT .............................................................................................................. i
CHAPTER 1 ............................................................................................................................... 6
INTRODUCTION ...................................................................................................................... 6
1.1 Introduction ................................................................................................................. 6
1.2 Background of the study ................................................................................................... 6
1.2.1 The important of quality audit performance......................................................... 7
1.2.2 Impact of information technology on audit judgments performance. .................. 8
1.2.3 Audit technology adoption by auditors ................................................................ 9
1.2 Research Problem ...................................................................................................... 10
1.4 Objective of the Study .................................................................................................... 11
1.5 Rationale of the Study .................................................................................................... 12
1.6 Contribution of the Study ............................................................................................... 13
1.7 Definition of Terms Used ............................................................................................... 13
1.8 Organization of the Thesis .............................................................................................. 14
CHAPTER 2 ............................................................................................................................. 15
LITERATURE REVIEW ......................................................................................................... 15
2.1 Introduction .................................................................................................................... 15
2.2 Technology Adoption ..................................................................................................... 15
2.2.1 Standards and regulation of Technology Adoption in Auditing ............................. 16
2.2.2 Computer-assisted audit tools and techniques and technology adoption ................. 20
2.2.3. Audit technology adoption ...................................................................................... 21
2.2.4 Audit Software Application in Audit Practices........................................................ 22
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2. 2.3 Individual Performance .................................................................................................. 24
2.3.1 Individual Performance Definition .......................................................................... 24
2.3.2 Relationship between Technology Adoption and Individual Performance ............. 25
2.4 Theoretical background .................................................................................................. 26
2.4.1 The History of Technology Acceptance Model ....................................................... 26
2.4.2 The Unified Theory of Acceptance and Use of Technology (UTAUT) Model....... 27
CHAPTER 3 ............................................................................................................................. 32
RESEARCH FRAMEWORK AND HYPOTHESES DEVELOPMENT ............................... 32
3.1 Introduction .................................................................................................................... 32
3.2 Research framework ....................................................................................................... 32
3.3 Operationalization and measurement of variables .......................................................... 34
3.3.1 Audit software adoption ........................................................................................... 34
3.3.2 Determinant factors of audit software adoption. ..................................................... 35
3.3.3 Individual performance ............................................................................................ 35
3.4 Hypotheses Development ............................................................................................... 35
3.4.1 Audit software application and audit performance .................................................. 36
3.4.2 Determinant factors of audit software use ............................................................... 36
CHAPTER 4 ............................................................................................................................. 44
RESEARCH METHODOLOGY ............................................................................................. 44
4.1 Introduction .................................................................................................................... 44
4.2 Research design .............................................................................................................. 44
4.3 Study One: Determinants of user intention to use Audit Command Language (ACL) and
impact to audit performance ................................................................................................. 47
4.3.1 The participants ........................................................................................................ 47
4.3.2 Data collection method ............................................................................................ 47
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3. 4.3.3 The questionnaire and variables development ......................................................... 47
4.3.4 Pre-test ..................................................................................................................... 47
4.3.5 Validity and reliability ............................................................................................. 48
4.3.6 Operationalisation of Variables ............................................................................... 49
4.3.7 Control Variables ..................................................................................................... 52
4.3.8 Techniques for Analysing Quantitative Data ........................................................... 52
4.4 Study Two: Determinant factors and impact of audit software application to audit
performance. ......................................................................................................................... 54
4.4.1 The participants ........................................................................................................ 54
4.4.2 Data collection method ............................................................................................ 54
4.4.3 The questionnaire and variables development ......................................................... 55
4.4.4 Pre-test ..................................................................................................................... 58
4.4.5 Validity and reliability ............................................................................................. 59
4.4.6 Operationalisation of Variables ............................................................................... 60
4.4.7 Control Variables ..................................................................................................... 62
4.4.8 Analysis of Structural Equation Modelling (SEM) ................................................. 62
4.5 Summary ......................................................................................................................... 70
CHAPTER 5 ............................................................................................................................. 71
RESULTS AND DISCUSSIONS OF FINDINGS .................................................................. 71
STUDY ONE: DETERMINANTS OF USER INTENTION TO USE AUDIT COMMAND
LANGUAGE (ACL) AND IMPACT TO AUDIT PERFORMANCE .................................... 71
5.1 Introduction .................................................................................................................... 71
5.2 Preliminary analysis ....................................................................................................... 71
5.2.1 Normality analysis ................................................................................................... 71
5.2.2 Reliability analysis ................................................................................................... 72
5.2.3 Factor analysis ......................................................................................................... 73
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4. 5.2.4 Descriptive Statistics of Participants ....................................................................... 73
5.2.5 Correlation analysis ................................................................................................. 74
5.3 Hypotheses Testing......................................................................................................... 75
5.4 Discussion of Findings ................................................................................................... 77
5.5 Summary ......................................................................................................................... 78
CHAPTER 6 ............................................................................................................................. 79
RESULTS AND DISCUSSIONS OF FINDINGS ............................................................... 79
STUDY TWO: DETERMINANT FACTORS AND IMPACT OF AUDIT SOFTWARE
APPLICATION TO AUDIT PERFORMANCE .................................................................. 79
6.1 Chapter Overview ........................................................................................................... 79
6.2 Descriptive Statistics of Participants .............................................................................. 79
6.2.1 Response rate .......................................................................................................... 79
6.2.2 Demographic Profile ............................................... Error! Bookmark not defined.
6.2.3 Descriptive Statistics of Constructs ......................... Error! Bookmark not defined.
6.3 Measurement Model ....................................................................................................... 85
6.3.1 Development of Measurement Model...................... Error! Bookmark not defined.
6.3.2 Congeneric Measurement Model ............................................................................. 87
6.4 Structural Model ............................................................................................................. 88
6.4.1 Assessment of the Structural Model ...................................................................... 107
6.5 Hypotheses Testing....................................................................................................... 108
6.6 Discussions of Findings ................................................................................................ 108
6.7 Summary ....................................................................................................................... 108
CHAPTER 7 ........................................................................................................................... 110
CONCLUSIONS, IMPLICATIONS, LIMITATION AND FUTURE RESEARCH ............ 110
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5. 7.1 Chapter Overview ......................................................................................................... 110
7.2 Discussions of Findings ................................................................................................ 110
7.3 Implication of the Findings........................................................................................... 110
7.3.1 Theoretical Implications ........................................................................................ 110
7.3.2 Practical Implications............................................................................................. 110
7.4 Limitations of the Study ............................................................................................... 110
7.5 Suggestions for Future Research .................................................................................. 110
7.6 Summary ....................................................................................................................... 110
REFERENCES ....................................................................................................................... 111
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6. CHAPTER 1
INTRODUCTION
1.1 Introduction
This thesis studies the level of audit software use in audit practice and it impact to individual
auditor performance. This study also looks into the determinant factors of audit software
adoption as tested in UnifiedTheory of Acceptance and Use of Technology (UTAUT) model.
The chapter aims at providing an overview of the thesis and its structural scheme. The first
section provides background to the research, followed by the research problem, research
objectives and questions. The rationale of carrying out this study is also explained in the
following section, together with brief explanation of contribution of this study to the
theoretical and practical point of view. The chapter concludes with an outline of the
organization of the thesis.
1.2 Background of the study
The emergence of information technology has had a tremendous impact on many areas of
human activities, including engineering, medicine, education as well as accounting and
auditing practices. Information technology (IT) or electronic data processing has changed the
way many organizations conduct business activities. In fact, IT is considered as one of the
major technological advances in businesses this decade. IT system has the ability to perform
many tasks, and IT providers continuously strive in finding new ways to enhance the use of
computer to promote efficiency and aid in decision making. Since many businesses at present
use computers to process their transactions, the auditing profession has to face with the need
and requirement to provide the audit services that can deal with the IT environment.
While the impact of information technology (IT) in business has grown exponentially, few
studies examine the use and perceived importance of IT, particularly outside of the largest
audit firms (Fischer 1996; Banker et al. 2002). This issue is important since IT has
dramatically changed the audit process. Standards now encourage auditors and audit firms to
adopt IT and use IT specialists when necessary (American Institute of Certified Public
Accountants [AICPA] 2001, 2002b, 2005, 2006; Public Company Accounting Oversight
Board [PCAOB] 2004b). However, auditing researchers and practitioners have little guidance
available on what IT has been or should be adopted.cp (Janvrin, Bierstaker, Lowe, 2007)
6

7. Although studies have suggested that the adoption of IT in audit practices would increase
auditor‟s productivity (Zhao et al. 2004), the adoption of audit technology by auditors is still
low (Liang et al., 2001; Debreceny et al., 2005; Curtis and Payne, 2008). Apart from
perception that adoption of IT in audit practices particularly audit software is costly,
complicated to learn and use, other possible reasons for lack of usage could be due to
unconvincing evidence of the merits in using audit technology to enhance audit performance
(Ismail and Zainol Abidin, 2009). Usability is not sufficient and large potential gains in
effectiveness and performance will not be realized if users are not willing to use information
system in general (Davis F. D., 1993) and audit software in particular, therefore, adoption is
crucial.
The usage of audit software can be increased provided auditors are convinced of the positive
impact of audit software on audit performance.Based on attitude-behaviour theory, Doll &
Torkzadeh (1998) describe a „system to value chain‟ of system success construct from beliefs,
to attitudes, to behaviour, to the social and economic impacts of information technology.
Torkzadeh & Doll(1999) argued that impact is a pivotal concept that embodied downstream
effects. It is difficult to imagine how information technology can be assessed without
evaluating the impact it may have on the individual‟s work. Thus, in audit practice to assess
the audit technology adoption impact is through the assessment of the auditor‟s individual
performance impact.
1.2.1 The important of quality audit performance.
Many accounting firms all over the world have faced various forms of litigation. At the same
time, the threat of litigation has demanded audit firms to maintain and improve the quality of
audit work (Manson et al., 2001). There is evidence that the use of audit software could give
rise to more quality audit. In fact the use of audit software in the audit process has greatly
increased in the last few years. This is true in the case of large audit firms who are motivated
by the desire to improve their efficiency to compete for clients. Manson et al., (2001) pointed
out that audit automation has been used in most areas of the audit processes, more extensively
by the Big Four audit firms than others.
Therefore, arguably, accounting firms in Malaysia should also strive for better audit quality to
be at par with the global accounting giants. This is especially important given that the service
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8. sector may prove to be the main pillar of our economy after natural resources run out.For the
audit firm to survive in this competitive era, the highest quality of the audit judgment must be
maintained. However the audit quality of audit firms has undergone severe criticism this
decade due to various financial crisis and management fraud. The fiasco of the Enron Scandal
in 2001 has further alarmed regulators and the public in many countries about audit quality
including various parties in Malaysia. Obviously, huge efforts by the audit firms need to be
taken in order to restore public confidence in the auditors‟ integrity and ability and
subsequently uphold the reputation of the profession. One of the ways to increase the public
confidence in the auditors is to provide quality audit judgments consistently. Speed and
accuracy of audit judgment would certainly help build public confidence in the auditors.
Although many audit firms are introducing audit technology in accounting processes, not
many are actually using the software available and even those who are using, are not using the
higher end software. There are many reasons for the reluctance to incorporate audit
technology in audit processes such as negative perception and unconvincing benefit of the use
of audit technology. Ismail and Zainol Abidin (2009) investigated the level of information
technology knowledge and information technology important in the specific context of audit
work among auditors in Malaysia. Their study suggested that that information technology
knowledge among the auditors is still at the lower level.
1.2.2 Impact of information technology on audit performance.
Within the information technology literature, there are many studies that have examined the
impact of information technology on firms‟ performance in different industries such as
manufacturing (Barua et al. 1995), banking (Parson et al. 1993), insurance (Francalanci and
Galal, 1998), healthcare (Menon et al. 2000), and retailing (Reardon et al. 1996). However,
empirical research to examine the impact of information technology on audit performance in
the accounting practices is under-research. To this date, only one study has examined the
impact of information technology on firms‟ productivity in producing quality audits (Banker,
Chang and Kao, 2002). The other studies examined the factors influencing the use of
information technology (Janvrin, Bierstaker and Lowe, 2009; Curtis and Payne, 2008 and
Merhout, 2007) and perception of use and belief in using the technology (Bhattacherjee 2001,
2004; Venkatesh and Morris 2000; and Davies et al 1989).
8

9. Although there is a general perception that information technology investments by public
accounting firms could improve firms‟ productivity in terms of consistent audit quality (Lee
and Arentzoff, 1991), the impact of information technology on auditors‟ performance is not
directly observable. To date there is still inadequate data available that could allow one to
examine in-depth processes involving the use of audit technology by auditors when
performing audit procedures (Zhang and Dhaliwal, 2009). Zhang and Dhalilal pointed out that
more data is needed to examine the influence of critical factors that may mediate or moderate
the performance value gained by the auditors when adopting audit technology.
1.2.3 Audit technology adoption by auditors
In audit situations where use of technology is optional, the implementation decision is
typically made by joint discussion between the audit manager and in-charge auditor (Houston,
1999) Auditing technology studies have primarily examined how the use of technology
affects cognitive processing and the resulting decisions auditors make.
Today, the extent to which auditors have adopted information technology, in particular audit
software in their audit process remains an empirical question (Arnold and Sutton 1998; Curtis
and Payne 2008; Janvrin et al. 2009). Audit software is an essential component of audit
technology, refers to computer tools that allow the extraction and analysis of data using
computer applications (Braun and Davis 2003). It is a type of computer program that performs
a wide range of audit management functions.
Although many studies have suggested effective usage of audit software would permits
auditors to increase their productivity in achieving quality audit judgments (Zhao et al. 2004),
the incorporation of audit technology by auditors is still low (Liang et al., 2001; Debreceny et
al., 2005; Shaikh, 2005; Curtis and Payne, 2008). Apart from the perception that the audit
software is costly, complicated to learn and use, other possible reasons for lack of usage could
be due to unconvincing evidence of the merits of using audit technology to enhance audit
performance (Ismail and Zainol Abidin, 2009). However, the usage of audit software can be
increased if they are convinced of the positive impact of audit software on audit judgment
performance.
This study seeks to identify the relationship between the adoptions of the audit software with
the individual audit performance. In other word, this study tries to justify that the individual
audit performance is increased with the increase in the level of audit software use among the
auditor. This study also aims at examining what influences the auditors to adopt audit
9

10. technology in their practices. The finding of this study hopefully will be able to clarify many
facts about the factors that the auditors normally consider for them to be comfortable enough
with the audit technology.
1.2 Research Problem
The relationship between investment in information technology (IT) and its effect on
organizational performance continues to interest academics and practitioners. Most
researches on audit technology success or its impact on business function such as auditing
have focused on a firm‟s level. There is still very limited empirical evidence that
investigate audit technology success from individual level dimension such as user
adoption of audit technology and its impact to audit performance. Such investigation is
required as uncertainty, resistance and dissatisfaction could occur among auditors due to
new working style or culture in audit technology environment. Uncertainty, resistance
and dissatisfaction would eventually, lead to the failure of the audit technology
implementation in the audit practices, and ultimately affect audit performance.Measuring
the audit technology adoption in term of level of use by auditorgives the management
more accurate feedback about user‟s acceptance towards audit technology.
"Whether Information Technology (IT) use leads to better individual performance has
always been an intriguing topic in IS field. However, not many studies examined the
Information Technology use/individual performance relationship given the significance of
the topic. Researchers and practitioners simply assumed that more IT use lead to better
individual performance. A review of the literature presented a different, rather conflicting,
picture than the conventional wisdom. The current study thus aims at investigating IT
use/individual performance relationship by focusing on the measurement issue i.e. how
different richness level measurement of IT use and individual performance affects the
use/individual performance relationship. Cp Shen 2009
Venkatest et al., (2003) stated that one of the most important directions for future research
is to tie this mature stream of research into other established streams of works. They
further stated that little to no research has addressed the link between user acceptance and
individual or organizational usage outcome. Thus, while it is often assumed that usage
will result in positive outcomes, this remain to be tested..see venkatesh page 470. Straub
(2009) pointed out that the TAM and
10

11. The extensive use of IT in audit process especially among big audit firms has been
motivated by the desire to improve efficiency to compete for clients (Manson et al.,
2001). Audit firms justified their large investments in audit automation by the need to
improve the quality of audit work and reduce audit costs. In other words, audit
automation can be viewed as simply another technology that audit firms employ to
maintain their competitiveness and profitability. Most of the studies on the technology
adoption have revealed that much of what is term audit automation consist merely of
word-processing and spreadsheet applications. There is little evidence on the manner in
which external auditors employ audit software in the pursuits of their audit objectives.
Although there were studies carry out on the use of Computer assisted Audit Tools and
Techniques (CAATTs) and Generalised Audit Software (GAS), two main terms often
associated with audit software, the focus was not on the use of audit software by the
external auditors. For example, Wehner and Jessup (2005), Debreceny, Lee, Neo and Toh
(2005) and Braun and Davis (2003) look into the adoption of GAS among internal
auditors. Therefore this study is carry out to fill the gap that exist in the literature.
1.4 Objective of the Study
The main objective of this study is to empirically test the impact of application audit software
in practice to the audit performance. Audit software application is measured based on the
auditor‟s normal practice; planning, testing and report writing. Audit performance is measured
based on respondent‟s perception of the audit software impact to the quality, speed,
productivity and effectiveness of the work. This study also attempted to empirically test the
factors contributing to the application of audit software in practice among auditors in
Malaysia. Factors that drive audit software application are classified under three
characteristics; individual, organizational and external factors.
More specifically, the research objectives of the present study are:
1. To examine the nature of relationship between application of audit software and
individual audit performance.
2. To investigate the extent to which individual factors (performance expectancy, effort
expectancy), organizational factors (organizational supports, facilitating condition,
technological and infrastructure support) and external factors (social influence and
11

12. client‟s technology) contributes to the application of audit software in practice among
the auditors.
3. To determine whether training moderates the individual factors (performance
expectancy and effort expectancy) and audit performance relationship.
4. To determine whether experience moderates the individual factors (performance
expectancy and effort expectancy) and audit performance relationship.
5. To investigate whether performance expectancy, effort expectancy, social influence
and facilitating conditions have influenced on the behavioral intention to adopt audit
software.
6. To determine whether specific knowledge and experience moderate the performance
expectance and effort expectancy relationship with the behavioral intention to adopt
audit software.
The research objective 1 to 4 answered by the Study Two, while the research objective 5 and
6 answered by the Study One.
1.5 Rationale of the Study
Based on the development in audit practice and research, this study aims to promote audit
quality through the adoption of audit technology specifically audit software in audit practices.
The relationship between investment in information technology (IT) and its effect on
organizational performance continues to interest academics and practitioners. In many cases,
due to the nature of the research design employed, this stream of research has been unable to
identify the impact of individual technologies on organizational performance (Devaraj and
Kohli, 2003). As individual performance plays a great role in organizational performance, this
study aim to investigate the impact of the use of audit software in audit practices among
auditors to the individual audit performance.
Many audit tasks, including workpaper documentation and review, increasinglyare performed
in electronic environments (e.g.. Croft 1992; Flagg, Glover, andSmith 1992; Knaster 1998;
Rothman 1997; Vezina 1997a, 1997b). Althoughmany believe that automation eliminates
human calculation errors, saves time andmoney, minimizes paper documentation, and
increases accuracy (Rothman 1997),there is little empirical data to support these claims.
Indeed, it has been observed,"although the use of IT to strengthen the audit function is
widespread, its impacton perfonnance has never been determined" (Vezina 1997a; p. 37).
Most disturbingis that performance may decline in electronic environments (Galletta, Hartzel,
12

13. Johnson, Joseph, and Rustagi 1997). Cp Bible, Graham and Rosman (2005)
While there is a developing literature demonstrating audit technology and its possible benefits
of use by the auditors (e.g., Liang et al. 2001; Shaikh 2005), there is little research
investigating the extent of usage among auditors in practice and the factors that associated
with its use (Curtis, Jenkin, Bedard and Deis 2009). There is also limited study to present
empirical tests of its efficiency and effectivenes or in general its impact to audit performance.
Among the little was study by Janvrin et al. (2008) whose explored audit IT use and its
perceived importance across several audit application and across diverse group of audit firms.
Their study reported that some applications are used extensively and some are not. It also
reported that auditors are varies in opinion about the importance of several audit application,
although not used extensively. However their study did not aim at the impact of audit
application use to the individual audit performance. Thus, to fill the gap, this study is carry
out to examine the auditors perception of the impact of audit technology used to their
individual audit performance.
Previous studies has shown that the use of audit technology among auditors in general is
somewhat low (Quoted?). There are many reasons contribute to this scenario. Perhaps
individual auditors are uncomfortable with certain computer-related procedures because of
their own IT knowledge and experience limitations. It may also be caused by insufficient IT
training and support from the firm that
1.6 Contribution of the Study
This research distinctly contributed to the fields of accounting and information system by
exploring the adoption of audit technology and its impact to the individual performance. The
evolution of information system and the popularity of the technology acceptance theories,
particularly TAM and UTAUT, have made the research in this area became targeted by many
information systems as well as accounting researchers. Most researches involving audit
technology focused on the factors that contribute to the adoption of technology.
1.7 Definition of Terms Used
There are various terms used in this study. For the ease of the reader‟s understanding, the
following sub-sections give definition on some terms which are of interest in this study. The
define terms are auditors, audit software, audit software application, audit performance,
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14. 1.7.1 Auditors
For the purpose of this study, auditors refer to “external” auditors or also known as the
“financial statement” auditors. External auditors are the individuals who work for an audit
firm that is completely independent of the company they are auditing (Leong, Coram,Cosserat
& Gill, 2001)
1.7.2 Audit software
1.8 Organization of the Thesis
The thesis is structured as follows: Chapter Two reviews relevant literature related to the
technology adoption in audit practices. Specifically it presents a comprehensive critical
review of the evolution of audit technology, the development of auditing standard pertaining
to the adoption of audit technology in audit practices, the impact of audit technology adoption
on the individual performance. The chapter then investigate existing literature on technology
adoption theories particularly the history of Technology Acceptance Model (TAM) and
Unified Theory of Acceptance and Use of Technology (UTAUT) model.
Chapter Three presents the research framework and hypotheses development. This chapter
discusses the components of the research variables, the operationalization as well as the
measurements of the variables, and lastly the proposed hypotheses.
Chapter Four highlights the research methodology adopted in this study. The chapter starts by
discussing the rationale for adopting quantitative survey as the method of the present study.
The chapter proceeds with the discussion of the research design for Study One followed by
discussion of Study Two. Basically, the discussion is concentrated on the participants, data
collection method, the questionnaire and variables development, pre-testing and
operationalization of variables in questionnaire. Validity and reliability measurement also
discuss as part of instrument development procedures. The chapter finally discusses the
techniques adopted for data analysis. Analysis of Variance (ANOVA) technique using SPSS
is used for data analysis in Study One and Structural Equation Modelling (SEM) using AMOS
is used for data analysis in Study Two.
Chapter Five details down the data analysis and report of Study One. It consists of three main
sections, which are the preliminary analysis, hypotheses testing and discussion of findings.
The preliminary analysis reports results related to the descriptive statistics of the sample,
normality and reliability analysis as well as factor analysis. The correlation analysis of the
14

15. independent and dependent variables is also reported in this section. The results of the
hypotheses testing using multiple regressions analysis is reported next.
Chapter Six details down the data analysis and report of results of Study Two. It consist of
five main sections; descriptive statistics, measurement model, structural model, hypotheses
testing and discussion of findings.
CHAPTER 2
LITERATURE REVIEW
2.1 Introduction
This section discusses literature pertaining to the scope of present research. Section 2.2
discusses about the technology adoption and auditing, specifically, the definition, histories
and evolution of auditing software, modules and auditing application. Next, section 2.3
discusses the individual performance as a consequence of audit technology adoption. Section
2.4 presents a review of the determinant factors of audit technology adoption as per UTAUT
model. This section also discusses the ….. and experience that play a role as moderator
variables.
2.2 Technology Adoption
The technology adoption domain is a well researched area in the information system.
Research in this area has explored topics such as the adoption of mobile banking (Zhou et al.,
2010), internet banking (Foon and Fah 2011; AbuShanad and Pearson 2007; Tan and Teo
2000), the use of websites (Schaik 2009), electronic commerce (Grandon and Mykytyn
2004), software application (Davis et al. 1989; Mathieson 1991), e-mail usage (Szajna 1996),
telemedicine applications (Chau and Hu 2001) and computer usage (Compeau and Higgins
1995).
In term of definition, technology adoption is defines as the decision to accept, or invest in, a
technology (Dasgupta, Granger and McGarry 2002).……….
Technology adoption has been studied at two levels; the first is at the organizational level and
the other is at the individual level. Oliveira and Martins (2011) reviewed theories for adoption
models at the firm level used in information systems (IS) literature and discussed two
prominent models; diffusion on innovation (DOI); and the technology, organization and
15

16. environment (TOE) framework. This study was motivated by the fact that there are not many
reviews of literature about the comparison of IT adoption models especially at the firm level.
Since most studies on IT adoption at the firm level are derived from these two theories
(Chong et at. 2009), such reviewed of the literature on these models aims to fill the gap.
At the individual level, the emphasis of the analysis is on the acceptance of the technology.
The Technology Acceptance Model (TAM) proposed by Davis (1989) has explained
acceptance of information technology. TAM states that and individual‟s adoption of
information technology is dependent on their perceived ease of use and perceived usefulness
of the technology. This model has been used and tested, and at a times modified, to study the
adoption of a number of different technologies in the past decade (
2.2.1 Standards and regulation of Technology Adoption in Auditing
Many business at present use computers to process their transactions, the auditing profession
has been faced with a need to provide increased guidance for audits conducted in an IT
environment. Various authoritative bodies, such as the American Institute of Certified Public
Accountants (AICPA) and the International Federation of Accountants and the Information
Systems Audit and Control Association (ISACA), have issued standards in this area to be
observed by their members in performing an IT audit (Yang and Guan, 2004). The following
sub sections explain the development of the standards relevant to technology adoption in
auditing.
2.2.1.1American Institute of Certified Public Accountants (AICPA) Standards.
Auditing Standards Board (ASB) is the senior technical body of American Institute of
Certified Public Accountants (AICPA) designated to issue pronouncements on auditing
matters. The ASB was formed in October 1978 and is responsible for the development and
promulgation of auditing standards and procedures known as Statement on Auditing
Standards (SAS) to be observed by members of the AICPA. The AICPA code of professional
conduct requires an AICPA member who performs an audit (the auditor) to comply with the
standards promulgated by the ASB. The auditors are expected to have sufficient knowledge of
the SASs to identify those that are applicable to them and should be prepared to justify
departures from the SASs.
16

17. Over the years AICPA has issued numbers of SAS that are related directly to IT and audit
even before ASB was formed.SAS No. 3, “The effects of on the auditor‟s study and
evaluation of internal control” (AICPA, 1974) was issued in conjunction with the need for a
framework concerning auditing procedures in examining the financial statements of entities
that use IT in accounting applications. This was the first bold step in defining the auditing
standard for IT system (Jancura and Lilly, 1977 as quoted in Yang and Guan, 2004). The
statement provided guidance for audit conducted in IT environments and required auditor to
evaluate computer during their audit.
According to SAS No. 3, the objectives of accounting control are the same in both a manual
system and an IT system. However, the organization and procedures required to accomplish
these objectives may be influenced by the method of data processing used. Therefore, the
procedures used by an auditor in the evaluation of accounting control to determine the nature,
timing and extent of audit procedures to be applied in the examination of financial statements
may be affected.
SAS No. 3 has been superseded by SAS No. 48, “The effects of computer processing on the
examination of financial statements”. It was effective for the examination of financial
statements for periods beginning after 31 August 1984. It also amended SAS No. 22 on
“Planning and supervision” (AICPA, 1978a), SAS No. 23 on “Analytical review procedures”
(AICPA, 1978b), and SAS No. 31 on “Evidential matter” (AICPA, 1980) to include
additional guidance for audits of financial statements in IT environments.
The ASB was in the opinion that auditors should consider the method of data processing used
by the client, including the use of computers, in essentially the same way and at the same time
that they consider other significant factors that could affect the audit. The use of IT could
affect the nature, timing and extent of audit procedures, so the auditor should consider these
effects throughout the audit. Therefore, the ASB felt that the guidance concerning the effect
of computer processing on audit of financial statements should be integrated with existing
guidance rather than presented separately. This is the primary reason why SAS No. 48
amended so many other existing statements.
Before amendment, SAS No. 22 on “Planning and supervision” requires the work in an audit
engagement to be adequately planned, and assistance, if any, to be properly supervised. SAS
No. 22 also provides guidance for the auditor making an examination in accordance with
GAAS. The engagement must be adequately planned and supervised for the auditor to achieve
17

18. the objectives of the examination, which is to gather the appropriate amount of sufficient
competent evidential matter to form the basis for an audit opinion on the financial statement.
SAS No. 48 came to place to amend SAS No. 22 by adding further planning considerations to
those already required. It requires the auditor to consider the methods (manual or
computerized) used by the client in processing significant accounting information.
SAS No. 23 which covers analytical review procedures superseded by SAS No. 56,
“Analytical procedures” (AICPA, 1988b), issued in April 1988. SAS No. 56 provides
guidance on the use of analytical procedures and requires the use of analytical procedures in
planning and overall review of all audits. When the client has an IT system, the auditor must
consider a particular factor in determining the usefulness of such procedures. This factor
relates to the increased availability of data prepared for management‟s use when computer
processing is used.
SAS No. 31 on “Evidential matters” states that once the auditor completes the study and
evaluation of internal control, substantive testing must be performed to obtain sufficient,
competent evidential matter on which the auditor can based his/her opinion. SAS No. 48
amended SAS No. 31 and states that audit evidence is not affected by computer processing,
but the methods used to gather audit evidence may be affected. In an IT environment, the
auditor may have to use computer-assisted audit techniques (CAAT) such as computer-aided
tracing and mapping, audit software, and embedded audit data collection to gather evidence.
The auditor will have to rely more heavily on CAAT methods for inspection and analytical
review procedures.
Later on, the AICPA issued a professional pronouncement on the implications of electronic
evidence, SAS No. 80, Amendment to Statement on Auditing Standard No. 31, Evidential
Matter. This amendment suggests that a system that predominantly consists of electronic
evidence, it might not be practical or possible to reduce detection risk to an acceptable level
by performing only substantive tests for one or more financial statement assertions. (Helms
and Fred, 2000). SAS No. 80 further notes that the auditor may find it difficult or impossible
to access certain information for inspection, inquiry, or confirmation without using IT. Hence
the auditor might use generalised audit software (GAS) or other computer-assisted audit
techniques to test system controls or access information.
SAS No. 94 “The effect of IT on the auditor‟s consideration of internal control in a financial
statement audit” (AICPA 2001) was released and came to effect for the audits of financial
18

19. statement beginning on or after 1 June 2001. SAS No. 94 provides guidance to auditors about
the effect of IT on internal control, and on the auditors‟ understanding of internal control and
assessment of control risk. This indicates that, in computer intensive environments, auditors
should assign one or more computer assurance specialist (CAS) to the engagement in order to
appropriately determine the effect of IT on the audit, gain an understanding of controls, and
design and perform test of IT controls. SAS No. 94 also requires that an auditor planning to
perform only substantive tests on an engagement must be satisfied that such an approach will
be effective (Curtis, Jenkin, Bedard, & Deis, 2009)
The AICPA, in addition to issuing several standards for IT-related auditing, also publishes
Top 10 Technologies list annually to build member awareness about important and emerging
technologies that will contribute to the profession. Auditor knowledge levels are clearly
specified in the International Standard on Auditing (ISA) 401, paragraph 4, (IFAC, 1999)
which states that the auditors should have sufficient knowledge of the computer information
system (CIS) to plan, direct, supervise and review the work performed. (Ismail and Abidin,
2009)
2.2.1.2International Federation of Accountants (IFAC)
2.2.1.3 Information Systems Audit and Control Association (ISACA)
ASACA was formed in 1969 to meet the unique, diverse and high technology needs of the
burgeoning information technology field. In an industry in which progress is measured in
nano-seconds, ISACA has moved with agility and speed to bridge the needs of the
international business community and the information technology controls profession.
2.2.1.4 Public Company Accounting Oversight Board (PCAOB)
Public Company Accounting Oversight Board (PCAOB) ...see curtis, bedard for training.
The Public Oversight Board (2000) pointed out that auditors‟ professional capabilities in an
accounting information system (AIS) and the evaluation ability of a computer assurance
specialist (CAS) are the main factors of auditing quality (Lin and Wang, 2011). Brazel and
Agoglias (2004) has examined the impact of auditors‟ professional capability on CAS and
AIS auditing system. The finding suggested that auditor with high AIS professionalism would
formulate higher standards in risk assessment of computerized auditing environments, while
the auditors of high CAS capability would be able to provide more accurate auditing reports.
19

20. 2.2.2 Computer-assisted audit tools and techniques and technology adoption
CAATTs are computer tools and techniques that an auditor uses as part of their audit
procedures to process data of audit significance contained in an entity‟s information systems
(Singleton, 2003). Lin and Wang (2011) further referred CAATTs to software that helps
auditors to conduct control and confirmation tests, analysis and verification of financial
statement data, and continuous monitoring and auditing. It can be widely applied in analysis
of financial data and error inspections to identify frauds or false statements. Braun and Davis
(2003) defined CAATTs more broadly to include any use of technology to assist in the
completion of an audit. This definition would include automated working papers and
traditional word processing applications. More importantly CAATTs are defined as computer-
assisted tools that permit auditors to increase their productivity, as well as that of the audit
function (Zhao, Yen and Cheng, 2004).
The advantage of the CAATTs systems is the automated auditing procedures for overall
auditing, rather than sample auditing. Thus, it can enable auditors to enhance the validity of
the data and results and also enable them to expand the scope of audit to a more high risk area
(Lin and Wang, 2011).
The failure of CAATTs to meet the expectation of the users could be due to several factors.
First, GAS or CAATTs lack of common interface with IT systems, such as file formats,
operating systems, and application programs (Shaikh, 2005). He started with interactive data
extraction and analysis, IDEA, one of the most popular GAS package that is able to extract
several file formats, such as ASCII, DBASE III, and other with common interface. He found
that the problem is that auditors will have to design one specialized audit software for each
Electronic Data Processing (EDP) system if the EDP system uses proprietary file formats or
different operating systems (Liang et al., 2001).
Second, other concurrent CAATTs often requires special audit software modules be
embedded at the EDP system design stage (Pathak, 2003). Therefore, the early involvement of
auditors at the time when the system is under development become necessary (Liang et al.,
2001; Tongren,1999). Furthermore, any changes in auditing policy may also require major
modification not only to individual audit software modules, but also entire EDP systems
(Wells, 2001; Liang et al., 2001). Thus, in summary, applying these advanced CAATTs is
usually very costly even if it is possible.
20

21. Third, as the auditees‟ EDP systems become more complex, it is essential for auditors to audit
through computers. The paper stream into and out of computers disappears and is replaced by
electronic data streams, which can only be analyzed in automated fashion. Most CAATTs
currently in use cannot directly access an auditee‟s live data. Auditors usually gather the
historical data file from the auditee‟s personnel. This situation creates the possibility to be
given manipulated or even fraudulent data.
From other perspective, CAATTs can be potrayed as the tools and techniques used to examine
directly the internal logic of an application as well as the tools and techniques used to draw
indirectly inferences upon an application‟s logic by examining the data processed by the
application (Hall, 2000). Of the five CAATTs that have been advanced in popular audit
literature, three – test data, integrated test facility, and parallel simulation – directly examine
the internal logic of the application. While the remaining are embedded audit module and
generalized audit software, examine the application‟s logic indirectly.
2.2.3.Audit technology adoption
Different authors have used different term to refer to the audit technology adoption in auditing
practices. Dowling and Leech (2007) and Dowling (2009) haveused the term audit support
system and decision aids to reflect the adoption of audit technology in auditing. Audit support
systems are the key terminology application deployed by audit firms to facilitate efficient and
effective audits (Dowling and Leech, 2007). They refers audit support systems to include
electronic workpapers, extensive help files, accounting and auditing stsndards, relevant
legislation, and decision aids. Dowling (2009) revealed that audit support systems are the
primary technology application audit firms deploy to control, facilitate, and support audit
work. His study investigates how several auditor, audit team, and audit firm factors influence
whether auditors use audit support systems the way audit firms intend them to be used.
Manson et al. (2001) used the term audit automation to reflect the IT use in the audit process.
They claimed that the increased use of IT is part of strategies being adopted by the big audit
firms to cope with a more competitive environment. Earlier, a survey by Manson et al. (1997)
found that audit automation was used in most aspects of the audit process, more extensively
by the Big Five audit firms than others, although much of what is termed audit automation
consists merely of word-processing and spreadsheet applications.
21

22. Generalised Audit Software (GAS) is the most common computer-assisted audit tool and
techniques (CAATTs) used in recent years (Braun and Davis, 2003; Singleton, 2006). GAS is
software package which is used by auditors to analyse and audit either live or extracted data
from a wide range of applications (Debreceny et al., 2005).Gas allows auditors to undertake
data extraction, querying, manipulation, summarization, and analytical task (Debreceny et al.,
2005). Two most of the popular GAS are Audit Command Language (ACL), Interactive Data
Extraction and Analysis (IDEA) (Braun and Davis, 2003) and Panaudit Plus (Debreceney et
al., 2005). These packages contain general modules to read existing computer files and
perform sophisticated manipulations of data contained in the files to accomplish audit task.
GAS also has other products like CA‟s Easytrieve, Statistical Analysis System (SAS), and
Statistical Package for Social Sciences (SPSS) (Singleton, 2006)
GAS is rapidly increased in use by internal auditors in their profession and audit staffs who
are involve requires background in data analytic technologies to perform their audit
tasks(Bagranoff and Vendrzyk, 2000). Debreceney et al., (2005) also found that GAS are
frequently being used in special investigation audits of two large local bank in Singapore. The
key reasons for the widespread use of GAS include its relative simplicity of use requiring
little specialized information systems knowledge and its adaptability to a variety of
environments and users (Braun and Davis, 2003).
While studies show that GAS is widely used by internal auditors, recent surveys show,
however that CPAs do not frequently and systematically use these CAATTs in practice
(Kalaba, 2002). Other surveys (1998-2001) indicate that both ex-post and concurrent
CAATTs are used primarily in internal audit settings by proprietary implementation. There
are several research concentrate on the adoption of GAS (Wehner and Jessup 2005;
Debreceney et al. 2005; Braun and Davis 2003), but only few papers analyzed about its usage
by external auditor.
2.2.4 Audit Software Application in Audit Practices
2.2.4.1 Client acceptance and audit planning
Technology is already having a major impact on audit planning. For example,
computers are used to generate clientspecific internal control templates to help identify
strengths and weaknesses in a system. To generate a client-specific internal control
template, auditors input data into a computer-based questionnaire developed by the
audit firm. In response to queries from the software, the computer can then be used to
22

23. analyze a client's business processes, determine controls that are present or missing
(based on a comparison with industry benchmarks), assess inherent and control risk,
and generate a detailed series of audit tests to be performed. As audit work continues,
the results of audit testing can then be entered into the software to
determine if the risks identified during planning have been appropriately addressed.
This helps to ensure that all significant risks have been addressed during the audit. cp
(Bierstaker et al. 2001)
Many firms have adopted a risk-based audit approach and developed or purchased
software to help the auditor gain an understanding of how external and internal risk
affect the audit. These software packages can also be used to help sell risk
identification and/or risk management services to existing and potential clients. cp
(Bierstaker et al. 2001)
In term of sampling....The new risk standards(SAS Nos. 104-111) suggest that auditors
use the computerized assisted auditing to select sampletransactions to audit from key
electronic files, sort transactions with specific characteristics, test an entirepopulation
instead of a sample, and obtain evidence about control effectiveness (AICPA 2006)
2.2.4.2 Audit substantive testing
Every audit engagement involves testing management‟s assertions (e.g. existence of
assets, liabilities and owner‟s equity, quality of earnings, reliability of internal control,
compliance with applicable laws and regulations) by gathering sufficient and
competent evidence.
2.2.4.3 Audit completion and report writing
A major advantage of electronic working paper s that enhances efficiency is taht
information can be shared among auditors at different locations through the use of e-
mail or remote access software (Debreceney et al., 2005). As needed, working papers
from prior years can easily be integrated into the current year working papers.
23

24. 2.3 Individual Performance
In many organizational life and other human affairs, individual performance plays a great role
in achieving the goals set. Different performance measurements are given in different
situations. For example, students in classroom at school or university, they are normally
evaluated based on their participation, assignments or capability to work in a group. In an
organizational context, the workers may be evaluated based on their productivity, quality of
their output, commitment skills, or integrity (Shen, 2009).
Due to the variety of context, individual performance was differently defined, so as the
measurements also different. In this section, individual performance definitions,
operationalization, measurements and it relationship with information technology that are
relevant to current study will be reviewed.
2.3.1 Individual Performance Definition
In recent years there has been a large increase in research related to individual performance
particularly in psychology, educational and learning, human resource as well as in general
management . Researchers have defined individual performance differently but consistent
over their respective area of study. In Information system (IS) literature however, researchers
seem to assume that performance is rather self-explanatory. This explains why in this research
area, clear definition of individual performance is still lacking.In addition, the review of IS
literature on the research in the individual performance found that the contexts, the construct
measured, or the theories based upon are not consistent.
Can put the summary of previous literature on Ind Performance in IS (in table form)
Most studies in IS literature developed their definitions of individual performance based on
“individual impact” definition from DeLone and McLean (1992). According to DeLone and
McLean (1992), IT use leads to three types of outcomes: user satisfaction, individual impact,
and organizational impact. Individual impact was defined as “the effect of information on the
behavior of the recipient”. Compared to individual performance, the term individual impact
was used loosely and it transcends mere individual performance and includes all other
outcomes under different contexts, for example, change in decision making productivity,
change in user activity, and user‟s perception of the importance of the system (DeLone and
McLean, 1992). Cp Chen Shen
24

25. In auditing
2.3.2 Relationship between Technology Adoption and Individual Performance
The relationship between IT use and individual performance has not been well addressed in
previous studies(Sundarraj & Vuong, 2004). The general believe is that more use of IT will
lead to better individual performance. This can be traced back to DeLone & McLean‟s work.
In their study, the measurements of information systems success fall into six major categories
– system quality, information quality, use, user satisfaction, organizational impact and also
individual impact. After that, several studies based their model on this study, and overlooking
testing the link between IT use and individual performance (Almutairi & Subramaniam, 2005;
Livari, 2005, McGill, Hobbs, & Klobas, 2003). However, prior researches failed to reach
consensus on the nature and strength of the relationship between IT use and individual
performance. Only conflicting results were presented from previous studies, some found IT
use improves individual performance and some found negative relationship.
Different researcher study different nature of IT use and examine the impact to individual
performance. In fact, the linkage between information technology and individual performance
has been an ongoing concern in IS research (Goodhue and Thompson, 1995). Most of the
studies in organizational setting show a positive relationship between IT use and individual
performance. For example, Goodhue (1988) reported that information systems have a positive
impact on performance only when there is correspondence between their functionality and the
task requirements of users.Devaraj & Kohli (2003) argued that the driver of IT impact is not
the investment in the technology, but the actual usage of technology. Their study on the use of
technology in hospital resulted in finding that technology usage was positively and
significantly associated with measures of hospital revenue and quality.
There were also studies reported the negative relationship between IT use and performance.
Bible, Graham, & Rosman, (2005) examined the impact of electronic work environments on
auditor performance. Their assessment was on whether audit technology affects decision
making in a workpaper review task. The result of an experiment revealed that the electronic
environment negatively impact auditors‟ performance. Auditors in the electronic work
environment found to be less able to identify seeded errors and to use them properly in
evaluating loan covenants as compared to the auditors in the traditional paper environment.
25

26. Many studies tested the association between “system use” and “individual impacts” and the
association was found to be significant in each of the studies. (DeLone and McLean,
2003)…to explain one by one..the seven studies
2.4 Theoretical background
The previous sections discussed the technology adoption and impact to individual
performance. For the technology to be of value it must be accepted and use. An important
model for studying technology adoption and usage is the Unified Theory of Acceptance and
Use of Technology (UTAUT).
The following sub-sections discuss the history of Technology Acceptance Model (TAM) of
which most of the basic variables tested in UTAUT model were adopted partly from this
model. This is followed by the discussion of the history of UTAUT model, the model tested in
this study. This sub-section lead the discussion into the introduction of the variables adopted
and tested in the current study.
2.4.1 The History of Technology Acceptance Model
Technology Acceptance Models (TAM) have been developed to measure system use,
acceptance, and user satisfaction of those systems (Davis, Bagozzi, & Warshaw, 1989). The
Davis model specifically focuses on information systems use and is based on the theory of
reasoned action (TRA) originally introduced by Ajzen and Fishbein in the early 80‟s (Ajzen
& Fishbein, 1980) and further refined by Ajzen as the extended TRA in 1991 (Ajzen, 1991).
TRA is a technology acceptance model that can be used to predict behavior in a wide variety
of situations, not just the adoption of information systems technology. Ajzen states that an
individual‟s beliefs influence his/her attitude towards various situations. The users‟ attitude
joins with subjective norms to shape the behavior intentions of each individual. (Cp Moran
2006)
This theory was further refined and called the theory of planned behavior (TPB) which is also
titled the extended theory of reasoned action. The TPB is a general behavior model which can
be used to study broader acceptance situations than the TAM but it has been applied to
information systems studies (Mathieson, 1991) & (Taylor & Todd, 2001). (Cp Moran 2006).
TPB includes many factors, or constructs, used to determine users‟ acceptance of innovations.
The three considerations are behavioral beliefs, normative beliefs, and control beliefs. These
are the users core beliefs about the consequences of the action, the expectations of others, and
26

27. beliefs about how the user controls, or does not control, the end result of the behavior. Table 1
further describes the model parameters.
2.4.2 The Unified Theory of Acceptance and Use of Technology (UTAUT) Model.
The Unified Theory of Acceptance and Use of Technology (UTAUT) integrated the concepts
of previous….This synthesized model created to present more comprehensive pictures of
acceptance process than any previous model able to do. This model emerged from the
combination of components from eight models previously established in IS literature, all of
which had their origins in psychology, sociology and communications. Theses eight models
are the Theory of Reasoned Action (TRA); Motivational Model (MM); Theory of Planned
Behaviour (TPB); Decomposed Theory of Planned Behaviour (DTPB); Technology
Acceptance Model (TAM); the Motivational Model (MM); Combined TAM and TPB (C-
TAM-TPB); Model of PC Utilization (MPCU); Innovation Diffusion Theory (IDT) ; and
Social Cognitive Theory (SCT). Each model attempts to predict and explain the user behavior
using a variety of independent variables.
Researchers have analysed and compared the competing technology acceptance theories and
models as noted above in order to identify the most promising ones in respect of the ability to
predict and explain individual behaviour towards the acceptance and usage of technology. The
UTAUT model formulated after eight models have been thoroughly revieved and empirically
compared. The UTAUT model explained about 70 percent of the variance in intention to use
technology, vastly superior to variance explained by the eight individual model (Rosen,
2005). Although the UTAUT model is relatively new, its suitability, validity and reliability in
technology adoption studies in different context and across the country has been proven
(AlAwadhi and Morris, 2008; Venkatesh and Zhang, 2010).
The UTAUT aims to explain user intentions to use an information system and subsequent
usage behavior. The main variables tested in the UTAUT model are: performance expectancy,
effort expectancy, social influence, and facilitating factors. Venkatesh et al. (2003) explain
performance expectancy as the degree of performance gain after using a new system or a
technology. This is an important variable in predicting user behavior. Considering the fact that
many people take in-service training courses in order to pursue career enhancement
opportunities, it is logical to offer them something new that would contribute to their job
27

28. performance. Therefore, high performance expectancy can encourage possible users to adopt
the new technology or the new system. Because of its importance, many theories have
adopted this construct in different ways.
The second main variable is effort expectancy. This variable measures the degree of effort
that a person needs to put forth when using a new technology or a new system. Research has
shown that users are more likely to adopt or use new technologies if they require a relatively
minimal amount of effort (Agarwal and Prasad, 1997; Konradt et al., 2006). It is likely that
resistance can be expected from the users, when employing a new technology, if the new
system requires them to work hard in order to learn it. It is a well known fact that many
people do not resist an innovation itself, but do resist learning a new thing that requires effort
instead of using a well known system. Therefore, this variable is also important in predicting
user behavior in terms of accepting or rejecting a new technology. Effort expectancy groups
several constructs fromother theories or models.
The third variable in the UTAUT is social influences. Social influences are the external and
internal factors that effect people when making a decision or displaying a behavior. In other
words, the degree that the people value significant other‟s opinions constitutes social
influences. Some people may feel pressure to comply with the proposed behavior, which in
this case can be the use of a new technology, while others may not. Social influence is used as
an independent variable in many models such as the “subjective norm in TRA, TAM2,
TPB/DTPB and C-TAMTPB, social factors in MPCU, and image in IDT” (Venkatesh et al.,
2003, p.451).
Finally, facilitating conditions is the last main variable in UTAUT. Venkatesh et al. (2003)
describes facilitating conditions as the state of readiness of the technological environment
with regards to its support for the user. Users may need support such as technical help in
using the new system or the new technology. If the technological environment offers such
support, users will be more likely to be in favor of using it. On the other hand, if the system
does not offer such support, it would be more difficult to encourage users to adopt the new
system or technology. Like the previous variables, facilitating conditions is included in earlier
models and theories, but in different formats. One example of this variable in a different
28

29. format is the perceived behavioral control variable used in TPB. This variable is also used in
TAM.
This model also incorporates certain variables as moderators of the relationship described
above. In particular UTAUT model tested several user variables and posits that gender, age,
experience, and voluntariness of use (the demographic characteristics), mediate the impact of
the four key constructs on usage intention and behavior (Venkatesh et al., 2003).
Gender, which has received some recent attention, is one of the key moderating influence in
technology adoption. Park, Yang, and Lehto (2007) examined the adoption of mobile
technologies byconsumers in China. In their study, they surveyed 221 Chinese people in order
to understandtheir perceptions regarding mobile communication technologies. The results of
their analysisrevealed the role that gender plays in terms of affecting user intentions. They
further revealed that male users were more influenced by performance expectancy than
female users. In other words,male users were more focused on increasing their gains from
mobile technologies than femaleusers. This finding is consistent with the study by Wang and
Shih (2009). On the other hand, effortexpectancy was higher for females than males.
Interestingly, experience did not significantlyaffect user intentions in this study.
Wang, Wu, and Wang (2009) investigated the acceptance of mobile learning technologies and
focused on gender and age issues to see whether they make a difference in users‟ perceptions.
In contrast to previous studies, Wang et al. (2009) did not find age or gender to have
asignificant moderating effect on performance expectancy. On the other hand, both gender
and
age significantly moderated effort expectancy and social influences. Wang et al. (2009)
reportedthat effort expectancy was more important for older users than younger ones. While
this findingwas not unexpected, as older users tend to look for less complex systems to
operate, themoderating affect of gender differences on social influences was really
unanticipated.Interestingly, male users‟ social influences scores were higher than that of
female users; that is,male users were more affected by the opinions of significant others than
female users.
Age....
29

30. Experience...
Many studies have explored the affects of moderating variables on user intentions.Koivumaki,
Ristola, and Kesti studied user perceptions towards mobile services (2008). Theresearchers
tested the University of Oulu‟s SmartRotuuari2 program on 243 people. The resultsindicated
that experience played a major role in determining user intentions.
Experiencepositively moderated performance expectancy and effort expectancy. On the other
hand,facilitating conditions was negatively moderated by experience. Particularly, Koivumaki
et al. (2008) noted that skilled users found the system useful and easy to use.Wang and Shih
(2009) study on 244 Taiwanese users of E-Government information kiosks alsoproduced
significant results in terms of moderating variables. According to the results,effort expectancy
was greater for old versus young users. Moreover, gender was significant in
determining user intentions. Wang and Shih found that performance expectancy was stronger
formen than women. Furthermore, social influences were stronger for women than men.
Voluntariness....
Figure 1 : UTAUT Model
Performance
Expectancy
Effort
Expectancy
Behavioral Use
Intention Behavior
Social
Influence
Facilitating
Condition
Gender Age Experience Voluntariness
of use
30

31. 2.4.2 Computer self-efficacy
2.4.2.1 Self-efficacy define
The concept of self-efficacy is derived from the work of Bandura and Social Cognitive
Theory (1986). Social Cognitive Theory (SCT) suggest that human behavior is reciprocally
influenced by environmental as well as cognitive factors, which include outcome expectation
and self-efficacy (Downey and McMurtrey, 2007). Self-efficacy is an individual‟s confidence
in their ability to successfully accomplish a given task or activity (Bandura, 1997). Self-
efficacy belief, therefore, determines how an individual feels, thinks, motivate themselves,
and how they behave and produce diverse effect through cognitive, motivational, effective,
and selection processes (Reid, 2008). Bandura (1986, 1997) holds that self-efficacy is more
than a belief in ability level; it also orchestrates the motivation necessary to conduct the
behavior. Self-efficacy helps determine what activities an individual engages in, the effort in
pursuing that activity, and the persistence in the face of adversity (Downey and McMurtrey,
2007).
General and specific CSE..see Argawal et al (2000) page 419..
Self-efficacy also applies to computing behavior. Several studies (Burkhardt and Brass, 1990;
Gist et al., 1989) have examined the relationship between self-efficacy with respect to using
computers and a variety of computer behaviors. Compeau and Higgins (1995b) define
computer self-efficacy as the judgment of one‟s capability to use an information technology.
They refer CSE as self-assessment of individual ability to apply computer skills to complete
the specific tasks. They remark on the relative paucity of prior research examining the
influence of self-efficacy in the context of computer training. Compeau and Higgins (1991,
1995a, 1995b, 199) are pioneers in studying the impact of CSE on human interaction with
computer.
This study extends current understanding of the concept of CSE in the context of the usage of
the audit software . See Argawal et al (2000) pg 419, para 4..explain about CSE concept in
audit software…and it‟s role as moderating factor to training.
31

32. CHAPTER 3
RESEARCH FRAMEWORK AND HYPOTHESES DEVELOPMENT
3.1 Introduction
The previous chapter has thoroughly reviewed the literature related to UTAUT and individual
performance. This chapter presents a research framework to determine the relationship
between research variables. The research variables are: (1) the level of audit software
application as technology adoption constructs (2) performance expectancy, effort expectancy,
social influence and facilitating conditions, organizational supports and training as the
antecedents of the technology adoption construct (3) experience and computer self-efficacy as
a moderator variables and (4) individual performance as the criterion variable. Then,
operationalization and specific measurements of these variables are discussed in detail.
Finally, the chapter discusses the research hypotheses to be tested.
3.2 Research framework
The comprehensive review of literature performed in the previous chapter found that most of
prior researches on technology adoption have stopped at the behavioral intention to adopt
information technology (…..quoted). Review of past studies revealed that only few researches
have been done on the actual usage and impact of technology, less little on the adoption of
audit software in audit practices. Several models have been proposed to predict the
technology adoption such as Technology Acceptance Model (Davis et al. 1989), Theory of
Planned Behavior (Ajzen, 1991) and Unified Theory of Acceptance and Use of Technology
(Venkatesh et al. 2003). However these models focus on whether a system is used, not how it
is used (Dowling, 2009). A number of studies attempted to extend the technology acceptance
study further into the use of technology in audit practice. For example, Bierstaker, Burnaby,
& Thibodeau, (2001) assessed thecurrent impact of technology on the auditprocess, and the
future implicationsof technological trends for the auditingprofession.
Based on the reviews done on the several technology adoption model as well as mid-range
theories related to the technology adoption, the theoretical foundation of this study is
premised on the UTAUT model which was tested by Venkatesh et al. (2003).Venkatesh et al.,
(2003)also highlighted that the directions for future research need to be directed more to the
32

33. outcome of the technology adoption. Until today, little to no research has addressed the link
between user acceptance and individual performance outcome. Thus, the assumption that
technology usage will always resulted in positive outcomes are still remains untested.
Therefore, this study is believed to be able to fill the gap that exists in the area of audit
technology.
Figure 2 : Research framework
Performance
Expectancy
Effort
Expectancy
Audit Individual
Social
Influence Software Performance
Application
Facilitating
Condition
Client
Technology
Organizational
Support
Training Experience
Computer Self-
Efficacy
Here need to explain about the adoption of audit software in auditing practices. Need to proof
that there is no attempt to investigate the adoption and impact to performance as well as the
determinat factors..
33

34. 3.3 Operationalization and measurement of variables
The present study is based on the UTAUT model which was developed and tested by
Venkatesh (2003). The focus of the study is to examine the factors that influence the
application of audit software amongst the auditors in different audit firms that adopting audit
software in practice. This study is also aimed to examine the impact of the audit software
application to the individual audit performance. As mentioned earlier (make sure this has been
mentioned), UTAUT model is in principle open for inclusion of other predictors if these
predictors can explain significant variance in the technology adoption,in an attempt to
provide an even richer understanding of technology adoption and usage behavior. Give
example other studies that recommended other variables..
As stated previously, the UTAUT model is open for inclusion (make sure this is mentioned
previously) of other variables pertinent to behavior usage of audit software. Therefore,
besides the main variables tested in UTAUT model, the conceptual framework of the present
study also includes computer self-efficacy (Compeau and Higgins,1995; Burkhardt and
Brass, 1990; Gist et al., 1989 ), training factorsas one of the elements influence the usage
behavior, elements, the present study also includes additional variables that can explain more .
The main variables introduced in UTAUT model are performance expectancy, effort
expectancy, social influence and facilitating condition. The study introduced two new
variables to the existing UTAUT model; client technology and computer self-efficacy. This
study tested the moderating effect of experience to the relationship of performance and effort
expectancy to the audit software application as tested in UTAUT model. To consider the
contribution of this study, new moderating variable was also tested to see the interaction
effect of training to the relationship of performance and effort expectancy to the audit
software application as tested in UTAUT model.
3.3.1 Audit software application
This study used the term audit software application to describe software used to assist auditors
in completing one or more tasks. Reviewed of prior literature and discussion held with
practitioners and academician resulted in the identification of 15 audit software applications.
The applications of audit software included those examined in previous research for example,
analytical procedures (Knechel, 1988),identifying samples (Kachelmeier & Messier, 1990).
They also included recent audit software applications in audit tasks, for example fraud review
(Bell & Carcello, 2000), testing online transactions (Wright, 2002). This study grouped the
audit applications
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35. 3.3.2 Determinant factors of audit software adoption.
3.3.3 Individual performance
The choice of performance measures is one of the most critical challenging facing
organizations. Performance measurement systems play a key role in developing strategic
plans, evaluating the achievement of organizational objectives, and compensating managers
(Ittner and Larcker, 1998). The individual performance is the ultimate dependent variable or
the criterion variable in this present research. Generally, individual performance is intended to
identify the extent to which users believe that adopting technology in performing the task
gives impact to their job performance. Individual performance also has been examined in its
specific aspects such as use of new technology enrich the work …………………In this
present research, individual performance is defined as the extent to which auditors believe
that using audit software in performing their audit task can increase the performance of audit
works.
Individual performance has been measured in audit technology context by several researchers.
For example ……
3.4 Hypotheses Development
According to Vierra, Pollack and Golez (1998) researchers normally restate research
questions as hypotheses because hypotheses can be subjected to empirical testing. This means
they can be tested using some form of research procedure such as observations or surveys. In
this way, the investigation can be confirmed if the prediction is empirically sound. (Singh,
Fook and Sidhu, 2006). The hypotheses of this study are developed on the basis of the
observation of past literatures according to the richness of measurement. When there are
mixed results from literature, this study consider the majority results to develop the
hypothesis.
Generally, the relationships investigated in the present research and their related hypotheses
can be classified as: (1) between audit software application and audit performance, (2)
between the determinant factors and audit software application, and (3) between the
determinants factors and audit software application with the interaction of moderator
variables. Details of the above are discussed in the following subsections.
35

36. 3.4.1 Audit software application and audit performance
Results of previous studies that have tested the IT use and individual performance relationship
shown mixed results. McGill, Hobbs, & Klobas (2003) and Livari (2005) did not find
significant relationship between IT use and individual performance. Both of them used
frequency as the IT use measure. For the performance measurements, McGill et al., (2003)
measured the subjective effectiveness, productivity, and performance of user-developd
application. Livari (2005) measured the perceived efficiency, productivity nad effectiveness
of a financial accounting system.
3.4.2 Determinant factors of audit software use
Although UTAUT is quite a new theory, dating to 2003, the literature review
revealedhundreds of research studies that use UTAUT as a theoretical background. This is an
indicator ofthe high degree of acceptance of UTAUT by scholars from many disciplines. In
the followingsection, some of these studies are detailed, providing information regarding
variables used in themodel and their level of significance in the respective studies. (cp
odabasi, 2010)
3.4.2.1 Performance Expectancy
This variable is considered to be the most important one in the UTAUT model. The
performance expectancy variable in the UTAUT predicts a positive relationship between
intention to use technology and gains in job performance. Actually,in most user acceptance
studies, performance-related variables such as perceived usefulnessattract the most attention.
In order to determine whether this variable is indeed the mostimportant variable, results from
studies in different disciplines are listed below.Anderson et al. (2006) applied the UTAUT
model to understanding the perceptions ofuniversity faculty toward tablet personal computer
(PC) usage. They surveyed 50 facultymembers by using web-based survey methods. As a
result of their study, Anderson et al. Foundthat performance expectancy was the “strongest
predictor” (Anderson et al. 2006, p.430). According to theirstudy, performance expectancy
positively affected the usage of the tablet PC. In other words, thefaculty who believed that
using a tablet PC increased their work performance tended to use thetablet PC more than the
faculty who thought otherwise.
Performance expectancy produced similar results in Wang and Shih (2009) study
ofinformation kiosk systems. They explored the perceptions of 244Taiwanese users in their
36

37. use of an E-Government information kiosk. Performance expectancy was operationalised as
the increased gain in accessing government relatedinformation and concluded that their
intention to use the information kiosks was heavilyinfluenced by their level of performance
expectancy. Therefore, increasing theperformance expectancy level of the users guaranteed a
high usage of the E-government kiosks.In addition to the E-Government and academic
environments, performance expectancywas also found to be the most influential factor in
adopting technology in business settings.
Wang, Archer, and Zheng (2006) examined the use of electronic marketplace (EM)
applications and theperceptions of their intended users. They associated performance
expectancy with greatereconomic benefits, such as increased customer contact and
improvement of business processes.They assumed that a system which increases the ability of
a company to contact buyers andsellers would be acceptable to that company. Furthermore, if
the system resulted in animprovement in business processes, it would attract more users.
Employing a case studymethodology with UTAUT as the theoretical background, Wang et.al.
(2006) determined thatperformance expectancy was a major variable in inducing the business
sector to use EM. In otherwords, the results of their study confirmed the significant affect of
performance expectancy onthe intention to use the EM.
In another study, Bandyopadhyay and Fraccastoro (2007) used the UTAUT model in order
tounderstand the perceptions of users towards prepayment metering systems. Theresearchers
hypothesized that consumers would prefer to use the prepayment meter technologyover
traditional payment methods, if they believe it is a useful system for managing theirelectricity
usage. The results of the study confirmed this hypothesis in the finding of asignificant
relationship between performance expectancy and the intention to use the system. Inother
words, people who thought that using the prepayment metering system would be helpful
inelectricity account management intended to use the system more than people who
thoughtotherwise. Furthermore, like many other scholars, Bandyopadhyay and Fraccastoro
(2007) determinedthat performance expectancy was the strongest variable within the
theoretical model.
With regards to the use of audit software, ..........(find study on audit software adoption)
Look at Bierstaker, burnaby and Thibodeu (2001) – explain the audit process ; audit
planning, testing ad documenttaion
Hence, the following is hypothesised:
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38. H2 : Auditors with high performance expectancy are associated with high usage of audit
software.
3.4.2.2Effort Expectancy
Like performance expectancy, effort expectancy is considered to be an importantdeterminant
of user intentions. In the user acceptance literature, most of the studies found asignificant
relationship between effort expectancy and intention. However, the relationship wasnot as
strong as with performance expectancy.Lin and Anol (2008) studied online social support and
network IT usage among 317Taiwan university students by employing the UTAUT. They
operationalized effort expectancy asthe degree of easiness in using the network IT. An
analysis of the survey revealed a significantrelationship between effort expectancy and user
intentions. Students who found the system easyto use were more likely to use the system than
those who found the system difficult to use.
Wu, Tao, and Yang (2007) also used UTAUT as a theoretical background in theirresearch
study. They studied the perceptions of users of 3G mobile communication systems
andhypothesized that effort expectancy would play a major role in increasing the intention
scores ofthe users. The researchers surveyed 394 users, by using an online questionnaire.
Using structuralequation modeling, Wu et al. found a significant relationship between effort
expectancy and theintention to use 3G mobile technologies. Therefore, they demonstrated
their hypothesis abouteffort expectancy boosting user intentions to be correct.
Im, Hong, and Kang (2007) studied mp3 player and internet banking technologies in
twodifferent settings, namely Korea and the United States. They wanted to compare
theperceptions of users from both countries to see whether there were any differences. Im et
al. (2007) collected data from 501 users including Korean college students and US
undergraduate students.They defined effort expectancy as the easiness of using the mp3
players and internet banking.Notwithstanding the differences in nationality, the results
demonstrated a significant relationshipbetween effort expectancy and user intentions for both
Korean and US students. Users whofound using both the mp3 players and Internet banking
easy had high intention scores.Although a majority of the studies demonstrate a significant
relationship between effortexpectancy and user intentions, there are a limited number of
studies that show otherwise.Anderson, Schwager, and Kerns (2006) studied the perceptions of
38

39. college faculty in their use of tablet PCs. They hypothesized that the ease of use of the tablet
PCs would positively affect userintentions. In other words, they expected to see higher
intention scores from users who found thetablet PC easy to operate. However, the study did
not produce any significant results in terms ofeffort expectancy, and their hypothesis was
rejected.
Adapting effort expectancy into the use of audit technology. Need to find support for this...
Hence, the following is hypothesised;
H3: Auditors with high effort expectancy are associated with high usage of audit software.
3.4.2.3 Social Influences
Marchewka et al. (2007) examined the Blackboard application which is a type ofeducational
software widely used by the university community. Their sampling frame wasuniversity
students both at the graduate and undergraduate levels. After surveying 132
universitystudents, they concluded that there was a significant relationship between social
influences andintention to use the Blackboard system. According to the results, students are
affected by theirsignificant others‟ opinions in terms of their use of the Blackboard system. If
they believe thatthey are encouraged by those people, they were more likely to use the system.
Armida (2008) used UTAUT as a theoretical framework for her study on VOIP systems.She
hypothesized that social influence scores would positively affect users‟ intention to use
theVOIP systems. In other words, users would decide whether to use the system based on
theopinions of people whom they consider important. Armida surveyed 475 respondents
fromvarious states in order to conduct her study. After statistical analysis, Armida concluded
thatsocial influences were a significant predictor of intention to use the VOIP systems.
Neufeld, Dong, and Higgins investigated the relationship between charismatic leadershipand
the adoption of information technology (2007). Neufeld et al. collected a sample of
207respondents from 7 organizations. and hypothesized that social influence was a
determinant of ITadoption. An analysis of their data resulted in positive scores for social
influences. In other
words, the results supported their hypothesis and found a significant relationship between
socialinfluences and intention to use the new IT system.
Adapting social influence into the use of audit software...Need to find past study to support..
39

40. Thus, the following is hypothesised;
H4: Social influence will significantly affect the use of audit software among auditors.
3.4.2.4 Facilitating Conditions
Facilitating conditions include the support function of the technology
implementations.Depending on the complexity of the systems, facilitating conditions can
affect intention to use asystem. Research findings from different studies display varying
results for facilitatingconditions. While some studies report significant relationships, other
studies found no significantrelationship between facilitating conditions and intention to use a
system.One of the studies that produced significant results was conducted by AlAwadhi and
Morris (2008). The researchers examined E-government services in Kuwait and surveyed
880university students in order to obtain their data. AlAwadhi and Morris (2008)
operationalizedfacilitating conditions by two measures: first, having the knowledge to use the
e-governmentservices and second, getting support when needed. The results of their study
indicate thatfacilitating conditions is a significant determinant in using a new system.
Wills, El-Gayar, and Bennett (2008) also found a significant relationship between
facilitatingconditions and intention to use a new system in their study of electronic medical
records.In this study, Wills et al. (2008) studied professionals working in the field of
healthcare. They definedhealthcare professionals as “registered nurses, physician assistants or
certified nurse practitionersin the state of South Dakota” (p398). They surveyed 52 healthcare
professionals in order toobtain their data. As noted earlier, the results demonstrated a
significant relationship betweenfacilitating conditions and intention to use electronic medical
records.
Al-Gahtani, Hubona, and Wang (2007) used the UTAUT model in order to understandthe
perceptions of Saudi Arabian users in terms of IT acceptance. Al-Gahtani et al. (2007)
surveyed1190 workers from companies that are located in four major cities. They
hypothesized thatfacilitating conditions would positively affect users‟ behaviors in terms of
using computersystems. However, the study did not produce significant results and their
hypothesis wasrejected.
Adapting fracilitating conditions into the use of audit software...Need to find past study to
support..
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