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Thesis writing

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Thesis writing

  1. 1. TABLE OF CONTENTTABLE OF CONTENT .............................................................................................................. iCHAPTER 1 ............................................................................................................................... 6INTRODUCTION ...................................................................................................................... 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 .............................................................................................. 14CHAPTER 2 ............................................................................................................................. 15LITERATURE 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 i
  2. 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....... 27CHAPTER 3 ............................................................................................................................. 32RESEARCH 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 ............................................................... 36CHAPTER 4 ............................................................................................................................. 44RESEARCH 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 ii
  3. 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 ......................................................................................................................... 70CHAPTER 5 ............................................................................................................................. 71RESULTS AND DISCUSSIONS OF FINDINGS .................................................................. 71STUDY ONE: DETERMINANTS OF USER INTENTION TO USE AUDIT COMMANDLANGUAGE (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 iii
  4. 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 ......................................................................................................................... 78CHAPTER 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 ....................................................................................................................... 108CHAPTER 7 ........................................................................................................................... 110CONCLUSIONS, IMPLICATIONS, LIMITATION AND FUTURE RESEARCH ............ 110 iv
  5. 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 ....................................................................................................................... 110REFERENCES ....................................................................................................................... 111 v
  6. 6. CHAPTER 1 INTRODUCTION1.1 IntroductionThis thesis studies the level of audit software use in audit practice and it impact to individualauditor performance. This study also looks into the determinant factors of audit softwareadoption 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 firstsection provides background to the research, followed by the research problem, researchobjectives and questions. The rationale of carrying out this study is also explained in thefollowing section, together with brief explanation of contribution of this study to thetheoretical and practical point of view. The chapter concludes with an outline of theorganization of the thesis.1.2 Background of the studyThe emergence of information technology has had a tremendous impact on many areas ofhuman activities, including engineering, medicine, education as well as accounting andauditing practices. Information technology (IT) or electronic data processing has changed theway many organizations conduct business activities. In fact, IT is considered as one of themajor technological advances in businesses this decade. IT system has the ability to performmany tasks, and IT providers continuously strive in finding new ways to enhance the use ofcomputer to promote efficiency and aid in decision making. Since many businesses at presentuse computers to process their transactions, the auditing profession has to face with the needand 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, fewstudies examine the use and perceived importance of IT, particularly outside of the largestaudit firms (Fischer 1996; Banker et al. 2002). This issue is important since IT hasdramatically changed the audit process. Standards now encourage auditors and audit firms toadopt IT and use IT specialists when necessary (American Institute of Certified PublicAccountants [AICPA] 2001, 2002b, 2005, 2006; Public Company Accounting OversightBoard [PCAOB] 2004b). However, auditing researchers and practitioners have little guidanceavailable on what IT has been or should be adopted.cp (Janvrin, Bierstaker, Lowe, 2007) 6
  7. 7. Although studies have suggested that the adoption of IT in audit practices would increaseauditor‟s productivity (Zhao et al. 2004), the adoption of audit technology by auditors is stilllow (Liang et al., 2001; Debreceny et al., 2005; Curtis and Payne, 2008). Apart fromperception 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 tounconvincing 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 ineffectiveness and performance will not be realized if users are not willing to use informationsystem in general (Davis F. D., 1993) and audit software in particular, therefore, adoption iscrucial.The usage of audit software can be increased provided auditors are convinced of the positiveimpact 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 downstreameffects. It is difficult to imagine how information technology can be assessed withoutevaluating the impact it may have on the individual‟s work. Thus, in audit practice to assessthe audit technology adoption impact is through the assessment of the auditor‟s individualperformance 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 sametime, the threat of litigation has demanded audit firms to maintain and improve the quality ofaudit work (Manson et al., 2001). There is evidence that the use of audit software could giverise to more quality audit. In fact the use of audit software in the audit process has greatlyincreased in the last few years. This is true in the case of large audit firms who are motivatedby the desire to improve their efficiency to compete for clients. Manson et al., (2001) pointedout that audit automation has been used in most areas of the audit processes, more extensivelyby the Big Four audit firms than others.Therefore, arguably, accounting firms in Malaysia should also strive for better audit quality tobe at par with the global accounting giants. This is especially important given that the service 7
  8. 8. sector may prove to be the main pillar of our economy after natural resources run out.For theaudit firm to survive in this competitive era, the highest quality of the audit judgment must bemaintained. However the audit quality of audit firms has undergone severe criticism thisdecade due to various financial crisis and management fraud. The fiasco of the Enron Scandalin 2001 has further alarmed regulators and the public in many countries about audit qualityincluding various parties in Malaysia. Obviously, huge efforts by the audit firms need to betaken in order to restore public confidence in the auditors‟ integrity and ability andsubsequently uphold the reputation of the profession. One of the ways to increase the publicconfidence in the auditors is to provide quality audit judgments consistently. Speed andaccuracy of audit judgment would certainly help build public confidence in the auditors.Although many audit firms are introducing audit technology in accounting processes, notmany are actually using the software available and even those who are using, are not using thehigher end software. There are many reasons for the reluctance to incorporate audittechnology in audit processes such as negative perception and unconvincing benefit of the useof audit technology. Ismail and Zainol Abidin (2009) investigated the level of informationtechnology knowledge and information technology important in the specific context of auditwork among auditors in Malaysia. Their study suggested that that information technologyknowledge 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 theimpact of information technology on firms‟ performance in different industries such asmanufacturing (Barua et al. 1995), banking (Parson et al. 1993), insurance (Francalanci andGalal, 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 inthe accounting practices is under-research. To this date, only one study has examined theimpact of information technology on firms‟ productivity in producing quality audits (Banker,Chang and Kao, 2002). The other studies examined the factors influencing the use ofinformation technology (Janvrin, Bierstaker and Lowe, 2009; Curtis and Payne, 2008 andMerhout, 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. 9. Although there is a general perception that information technology investments by publicaccounting firms could improve firms‟ productivity in terms of consistent audit quality (Leeand Arentzoff, 1991), the impact of information technology on auditors‟ performance is notdirectly observable. To date there is still inadequate data available that could allow one toexamine in-depth processes involving the use of audit technology by auditors whenperforming audit procedures (Zhang and Dhaliwal, 2009). Zhang and Dhalilal pointed out thatmore data is needed to examine the influence of critical factors that may mediate or moderatethe performance value gained by the auditors when adopting audit technology.1.2.3 Audit technology adoption by auditorsIn audit situations where use of technology is optional, the implementation decision istypically made by joint discussion between the audit manager and in-charge auditor (Houston,1999) Auditing technology studies have primarily examined how the use of technologyaffects cognitive processing and the resulting decisions auditors make.Today, the extent to which auditors have adopted information technology, in particular auditsoftware in their audit process remains an empirical question (Arnold and Sutton 1998; Curtisand Payne 2008; Janvrin et al. 2009). Audit software is an essential component of audittechnology, refers to computer tools that allow the extraction and analysis of data usingcomputer applications (Braun and Davis 2003). It is a type of computer program that performsa wide range of audit management functions.Although many studies have suggested effective usage of audit software would permitsauditors 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 etal., 2005; Shaikh, 2005; Curtis and Payne, 2008). Apart from the perception that the auditsoftware is costly, complicated to learn and use, other possible reasons for lack of usage couldbe due to unconvincing evidence of the merits of using audit technology to enhance auditperformance (Ismail and Zainol Abidin, 2009). However, the usage of audit software can beincreased if they are convinced of the positive impact of audit software on audit judgmentperformance.This study seeks to identify the relationship between the adoptions of the audit software withthe individual audit performance. In other word, this study tries to justify that the individualaudit performance is increased with the increase in the level of audit software use among theauditor. This study also aims at examining what influences the auditors to adopt audit 9
  10. 10. technology in their practices. The finding of this study hopefully will be able to clarify manyfacts about the factors that the auditors normally consider for them to be comfortable enoughwith 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 hasalways been an intriguing topic in IS field. However, not many studies examined theInformation Technology use/individual performance relationship given the significance ofthe topic. Researchers and practitioners simply assumed that more IT use lead to betterindividual performance. A review of the literature presented a different, rather conflicting,picture than the conventional wisdom. The current study thus aims at investigating ITuse/individual performance relationship by focusing on the measurement issue i.e. howdifferent 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. 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 StudyThe main objective of this study is to empirically test the impact of application audit softwarein practice to the audit performance. Audit software application is measured based on theauditor‟s normal practice; planning, testing and report writing. Audit performance is measuredbased 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 thefactors contributing to the application of audit software in practice among auditors inMalaysia. Factors that drive audit software application are classified under threecharacteristics; 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. 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 and6 answered by the Study One.1.5 Rationale of the StudyBased on the development in audit practice and research, this study aims to promote auditquality through the adoption of audit technology specifically audit software in audit practices.The relationship between investment in information technology (IT) and its effect onorganizational 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 toidentify the impact of individual technologies on organizational performance (Devaraj andKohli, 2003). As individual performance plays a great role in organizational performance, thisstudy aim to investigate the impact of the use of audit software in audit practices amongauditors to the individual audit performance.Many audit tasks, including workpaper documentation and review, increasinglyare performedin electronic environments (e.g.. Croft 1992; Flagg, Glover, andSmith 1992; Knaster 1998;Rothman 1997; Vezina 1997a, 1997b). Althoughmany believe that automation eliminateshuman calculation errors, saves time andmoney, minimizes paper documentation, andincreases 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 iswidespread, its impacton perfonnance has never been determined" (Vezina 1997a; p. 37).Most disturbingis that performance may decline in electronic environments (Galletta, Hartzel, 12
  13. 13. Johnson, Joseph, and Rustagi 1997). Cp Bible, Graham and Rosman (2005)While there is a developing literature demonstrating audit technology and its possible benefitsof use by the auditors (e.g., Liang et al. 2001; Shaikh 2005), there is little researchinvestigating the extent of usage among auditors in practice and the factors that associatedwith its use (Curtis, Jenkin, Bedard and Deis 2009). There is also limited study to presentempirical 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 itsperceived 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 alsoreported 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 auditapplication use to the individual audit performance. Thus, to fill the gap, this study is carryout to examine the auditors perception of the impact of audit technology used to theirindividual audit performance.Previous studies has shown that the use of audit technology among auditors in general issomewhat low (Quoted?). There are many reasons contribute to this scenario. Perhapsindividual auditors are uncomfortable with certain computer-related procedures because oftheir own IT knowledge and experience limitations. It may also be caused by insufficient ITtraining and support from the firm that1.6 Contribution of the StudyThis research distinctly contributed to the fields of accounting and information system byexploring the adoption of audit technology and its impact to the individual performance. Theevolution 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 manyinformation systems as well as accounting researchers. Most researches involving audittechnology focused on the factors that contribute to the adoption of technology.1.7 Definition of Terms UsedThere are various terms used in this study. For the ease of the reader‟s understanding, thefollowing sub-sections give definition on some terms which are of interest in this study. Thedefine terms are auditors, audit software, audit software application, audit performance, 13
  14. 14. 1.7.1 AuditorsFor 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 auditfirm that is completely independent of the company they are auditing (Leong, Coram,Cosserat& Gill, 2001)1.7.2 Audit software1.8 Organization of the ThesisThe thesis is structured as follows: Chapter Two reviews relevant literature related to thetechnology adoption in audit practices. Specifically it presents a comprehensive criticalreview of the evolution of audit technology, the development of auditing standard pertainingto the adoption of audit technology in audit practices, the impact of audit technology adoptionon the individual performance. The chapter then investigate existing literature on technologyadoption theories particularly the history of Technology Acceptance Model (TAM) andUnified Theory of Acceptance and Use of Technology (UTAUT) model.Chapter Three presents the research framework and hypotheses development. This chapterdiscusses the components of the research variables, the operationalization as well as themeasurements of the variables, and lastly the proposed hypotheses.Chapter Four highlights the research methodology adopted in this study. The chapter starts bydiscussing 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 bydiscussion of Study Two. Basically, the discussion is concentrated on the participants, datacollection method, the questionnaire and variables development, pre-testing andoperationalization of variables in questionnaire. Validity and reliability measurement alsodiscuss as part of instrument development procedures. The chapter finally discusses thetechniques adopted for data analysis. Analysis of Variance (ANOVA) technique using SPSSis used for data analysis in Study One and Structural Equation Modelling (SEM) using AMOSis used for data analysis in Study Two.Chapter Five details down the data analysis and report of Study One. It consists of three mainsections, 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. 15. independent and dependent variables is also reported in this section. The results of thehypotheses testing using multiple regressions analysis is reported next.Chapter Six details down the data analysis and report of results of Study Two. It consist offive main sections; descriptive statistics, measurement model, structural model, hypothesestesting and discussion of findings. CHAPTER 2 LITERATURE REVIEW2.1 IntroductionThis section discusses literature pertaining to the scope of present research. Section 2.2discusses about the technology adoption and auditing, specifically, the definition, historiesand evolution of auditing software, modules and auditing application. Next, section 2.3discusses the individual performance as a consequence of audit technology adoption. Section2.4 presents a review of the determinant factors of audit technology adoption as per UTAUTmodel. This section also discusses the ….. and experience that play a role as moderatorvariables.2.2 Technology AdoptionThe 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 Teo2000), the use of websites (Schaik 2009), electronic commerce (Grandon and Mykytyn2004), software application (Davis et al. 1989; Mathieson 1991), e-mail usage (Szajna 1996),telemedicine applications (Chau and Hu 2001) and computer usage (Compeau and Higgins1995).In term of definition, technology adoption is defines as the decision to accept, or invest in, atechnology (Dasgupta, Granger and McGarry 2002).……….Technology adoption has been studied at two levels; the first is at the organizational level andthe other is at the individual level. Oliveira and Martins (2011) reviewed theories for adoptionmodels at the firm level used in information systems (IS) literature and discussed twoprominent models; diffusion on innovation (DOI); and the technology, organization and 15
  16. 16. environment (TOE) framework. This study was motivated by the fact that there are not manyreviews 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 explainedacceptance of information technology. TAM states that and individual‟s adoption ofinformation technology is dependent on their perceived ease of use and perceived usefulnessof the technology. This model has been used and tested, and at a times modified, to study theadoption of a number of different technologies in the past decade (2.2.1 Standards and regulation of Technology Adoption in AuditingMany business at present use computers to process their transactions, the auditing professionhas been faced with a need to provide increased guidance for audits conducted in an ITenvironment. Various authoritative bodies, such as the American Institute of Certified PublicAccountants (AICPA) and the International Federation of Accountants and the InformationSystems Audit and Control Association (ISACA), have issued standards in this area to beobserved by their members in performing an IT audit (Yang and Guan, 2004). The followingsub sections explain the development of the standards relevant to technology adoption inauditing.2.2.1.1American Institute of Certified Public Accountants (AICPA) Standards.Auditing Standards Board (ASB) is the senior technical body of American Institute ofCertified Public Accountants (AICPA) designated to issue pronouncements on auditingmatters. The ASB was formed in October 1978 and is responsible for the development andpromulgation of auditing standards and procedures known as Statement on AuditingStandards (SAS) to be observed by members of the AICPA. The AICPA code of professionalconduct requires an AICPA member who performs an audit (the auditor) to comply with thestandards promulgated by the ASB. The auditors are expected to have sufficient knowledge ofthe SASs to identify those that are applicable to them and should be prepared to justifydepartures from the SASs. 16
  17. 17. Over the years AICPA has issued numbers of SAS that are related directly to IT and auditeven before ASB was formed.SAS No. 3, “The effects of on the auditor‟s study andevaluation of internal control” (AICPA, 1974) was issued in conjunction with the need for aframework concerning auditing procedures in examining the financial statements of entitiesthat use IT in accounting applications. This was the first bold step in defining the auditingstandard for IT system (Jancura and Lilly, 1977 as quoted in Yang and Guan, 2004). Thestatement provided guidance for audit conducted in IT environments and required auditor toevaluate computer during their audit.According to SAS No. 3, the objectives of accounting control are the same in both a manualsystem and an IT system. However, the organization and procedures required to accomplishthese objectives may be influenced by the method of data processing used. Therefore, theprocedures 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 statementsmay be affected.SAS No. 3 has been superseded by SAS No. 48, “The effects of computer processing on theexamination of financial statements”. It was effective for the examination of financialstatements 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 includeadditional guidance for audits of financial statements in IT environments.The ASB was in the opinion that auditors should consider the method of data processing usedby the client, including the use of computers, in essentially the same way and at the same timethat they consider other significant factors that could affect the audit. The use of IT couldaffect the nature, timing and extent of audit procedures, so the auditor should consider theseeffects throughout the audit. Therefore, the ASB felt that the guidance concerning the effectof computer processing on audit of financial statements should be integrated with existingguidance rather than presented separately. This is the primary reason why SAS No. 48amended so many other existing statements.Before amendment, SAS No. 22 on “Planning and supervision” requires the work in an auditengagement to be adequately planned, and assistance, if any, to be properly supervised. SASNo. 22 also provides guidance for the auditor making an examination in accordance withGAAS. The engagement must be adequately planned and supervised for the auditor to achieve 17
  18. 18. the objectives of the examination, which is to gather the appropriate amount of sufficientcompetent 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 tothose already required. It requires the auditor to consider the methods (manual orcomputerized) 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 providesguidance on the use of analytical procedures and requires the use of analytical procedures inplanning and overall review of all audits. When the client has an IT system, the auditor mustconsider a particular factor in determining the usefulness of such procedures. This factorrelates to the increased availability of data prepared for management‟s use when computerprocessing is used.SAS No. 31 on “Evidential matters” states that once the auditor completes the study andevaluation 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. 48amended 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, theauditor may have to use computer-assisted audit techniques (CAAT) such as computer-aidedtracing 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 analyticalreview procedures.Later on, the AICPA issued a professional pronouncement on the implications of electronicevidence, SAS No. 80, Amendment to Statement on Auditing Standard No. 31, EvidentialMatter. This amendment suggests that a system that predominantly consists of electronicevidence, it might not be practical or possible to reduce detection risk to an acceptable levelby performing only substantive tests for one or more financial statement assertions. (Helmsand Fred, 2000). SAS No. 80 further notes that the auditor may find it difficult or impossibleto access certain information for inspection, inquiry, or confirmation without using IT. Hencethe auditor might use generalised audit software (GAS) or other computer-assisted audittechniques to test system controls or access information.SAS No. 94 “The effect of IT on the auditor‟s consideration of internal control in a financialstatement audit” (AICPA 2001) was released and came to effect for the audits of financial 18
  19. 19. statement beginning on or after 1 June 2001. SAS No. 94 provides guidance to auditors aboutthe effect of IT on internal control, and on the auditors‟ understanding of internal control andassessment of control risk. This indicates that, in computer intensive environments, auditorsshould assign one or more computer assurance specialist (CAS) to the engagement in order toappropriately determine the effect of IT on the audit, gain an understanding of controls, anddesign and perform test of IT controls. SAS No. 94 also requires that an auditor planning toperform only substantive tests on an engagement must be satisfied that such an approach willbe effective (Curtis, Jenkin, Bedard, & Deis, 2009)The AICPA, in addition to issuing several standards for IT-related auditing, also publishesTop 10 Technologies list annually to build member awareness about important and emergingtechnologies that will contribute to the profession. Auditor knowledge levels are clearlyspecified in the International Standard on Auditing (ISA) 401, paragraph 4, (IFAC, 1999)which states that the auditors should have sufficient knowledge of the computer informationsystem (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 theburgeoning information technology field. In an industry in which progress is measured innano-seconds, ISACA has moved with agility and speed to bridge the needs of theinternational 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 anaccounting information system (AIS) and the evaluation ability of a computer assurancespecialist (CAS) are the main factors of auditing quality (Lin and Wang, 2011). Brazel andAgoglias (2004) has examined the impact of auditors‟ professional capability on CAS andAIS auditing system. The finding suggested that auditor with high AIS professionalism wouldformulate higher standards in risk assessment of computerized auditing environments, whilethe auditors of high CAS capability would be able to provide more accurate auditing reports. 19
  20. 20. 2.2.2 Computer-assisted audit tools and techniques and technology adoptionCAATTs are computer tools and techniques that an auditor uses as part of their auditprocedures 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 helpsauditors to conduct control and confirmation tests, analysis and verification of financialstatement data, and continuous monitoring and auditing. It can be widely applied in analysisof 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 thecompletion of an audit. This definition would include automated working papers andtraditional 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 auditfunction (Zhao, Yen and Cheng, 2004).The advantage of the CAATTs systems is the automated auditing procedures for overallauditing, rather than sample auditing. Thus, it can enable auditors to enhance the validity ofthe 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 dataextraction and analysis, IDEA, one of the most popular GAS package that is able to extractseveral file formats, such as ASCII, DBASE III, and other with common interface. He foundthat the problem is that auditors will have to design one specialized audit software for eachElectronic Data Processing (EDP) system if the EDP system uses proprietary file formats ordifferent operating systems (Liang et al., 2001).Second, other concurrent CAATTs often requires special audit software modules beembedded at the EDP system design stage (Pathak, 2003). Therefore, the early involvement ofauditors 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 majormodification 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 isusually very costly even if it is possible. 20
  21. 21. Third, as the auditees‟ EDP systems become more complex, it is essential for auditors to auditthrough computers. The paper stream into and out of computers disappears and is replaced byelectronic data streams, which can only be analyzed in automated fashion. Most CAATTscurrently in use cannot directly access an auditee‟s live data. Auditors usually gather thehistorical data file from the auditee‟s personnel. This situation creates the possibility to begiven manipulated or even fraudulent data.From other perspective, CAATTs can be potrayed as the tools and techniques used to examinedirectly the internal logic of an application as well as the tools and techniques used to drawindirectly inferences upon an application‟s logic by examining the data processed by theapplication (Hall, 2000). Of the five CAATTs that have been advanced in popular auditliterature, three – test data, integrated test facility, and parallel simulation – directly examinethe internal logic of the application. While the remaining are embedded audit module andgeneralized audit software, examine the application‟s logic indirectly.2.2.3.Audit technology adoptionDifferent authors have used different term to refer to the audit technology adoption in auditingpractices. Dowling and Leech (2007) and Dowling (2009) haveused the term audit supportsystem and decision aids to reflect the adoption of audit technology in auditing. Audit supportsystems are the key terminology application deployed by audit firms to facilitate efficient andeffective audits (Dowling and Leech, 2007). They refers audit support systems to includeelectronic workpapers, extensive help files, accounting and auditing stsndards, relevantlegislation, and decision aids. Dowling (2009) revealed that audit support systems are theprimary technology application audit firms deploy to control, facilitate, and support auditwork. His study investigates how several auditor, audit team, and audit firm factors influencewhether 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 auditfirms 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 extensivelyby the Big Five audit firms than others, although much of what is termed audit automationconsists merely of word-processing and spreadsheet applications. 21
  22. 22. Generalised Audit Software (GAS) is the most common computer-assisted audit tool andtechniques (CAATTs) used in recent years (Braun and Davis, 2003; Singleton, 2006). GAS issoftware package which is used by auditors to analyse and audit either live or extracted datafrom a wide range of applications (Debreceny et al., 2005).Gas allows auditors to undertakedata extraction, querying, manipulation, summarization, and analytical task (Debreceny et al.,2005). Two most of the popular GAS are Audit Command Language (ACL), Interactive DataExtraction and Analysis (IDEA) (Braun and Davis, 2003) and Panaudit Plus (Debreceney etal., 2005). These packages contain general modules to read existing computer files andperform 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), andStatistical Package for Social Sciences (SPSS) (Singleton, 2006)GAS is rapidly increased in use by internal auditors in their profession and audit staffs whoare involve requires background in data analytic technologies to perform their audittasks(Bagranoff and Vendrzyk, 2000). Debreceney et al., (2005) also found that GAS arefrequently being used in special investigation audits of two large local bank in Singapore. Thekey reasons for the widespread use of GAS include its relative simplicity of use requiringlittle specialized information systems knowledge and its adaptability to a variety ofenvironments 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 concurrentCAATTs are used primarily in internal audit settings by proprietary implementation. Thereare 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 usageby external auditor.2.2.4 Audit Software Application in Audit Practices2.2.4.1 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. 23. analyze a clients 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. 24. 2.3 Individual PerformanceIn many organizational life and other human affairs, individual performance plays a great rolein achieving the goals set. Different performance measurements are given in differentsituations. For example, students in classroom at school or university, they are normallyevaluated based on their participation, assignments or capability to work in a group. In anorganizational context, the workers may be evaluated based on their productivity, quality oftheir output, commitment skills, or integrity (Shen, 2009).Due to the variety of context, individual performance was differently defined, so as themeasurements also different. In this section, individual performance definitions,operationalization, measurements and it relationship with information technology that arerelevant to current study will be reviewed.2.3.1 Individual Performance DefinitionIn recent years there has been a large increase in research related to individual performanceparticularly in psychology, educational and learning, human resource as well as in generalmanagement . Researchers have defined individual performance differently but consistentover their respective area of study. In Information system (IS) literature however, researchersseem to assume that performance is rather self-explanatory. This explains why in this researcharea, clear definition of individual performance is still lacking.In addition, the review of ISliterature on the research in the individual performance found that the contexts, the constructmeasured, 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 andMcLean (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 thebehavior of the recipient”. Compared to individual performance, the term individual impactwas used loosely and it transcends mere individual performance and includes all otheroutcomes 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 andMcLean, 1992). Cp Chen Shen 24
  25. 25. In auditing2.3.2 Relationship between Technology Adoption and Individual PerformanceThe relationship between IT use and individual performance has not been well addressed inprevious studies(Sundarraj & Vuong, 2004). The general believe is that more use of IT willlead 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 alsoindividual impact. After that, several studies based their model on this study, and overlookingtesting the link between IT use and individual performance (Almutairi & Subramaniam, 2005;Livari, 2005, McGill, Hobbs, & Klobas, 2003). However, prior researches failed to reachconsensus on the nature and strength of the relationship between IT use and individualperformance. Only conflicting results were presented from previous studies, some found ITuse improves individual performance and some found negative relationship.Different researcher study different nature of IT use and examine the impact to individualperformance. In fact, the linkage between information technology and individual performancehas been an ongoing concern in IS research (Goodhue and Thompson, 1995). Most of thestudies in organizational setting show a positive relationship between IT use and individualperformance. For example, Goodhue (1988) reported that information systems have a positiveimpact on performance only when there is correspondence between their functionality and thetask requirements of users.Devaraj & Kohli (2003) argued that the driver of IT impact is notthe investment in the technology, but the actual usage of technology. Their study on the use oftechnology in hospital resulted in finding that technology usage was positively andsignificantly 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 onauditor performance. Their assessment was on whether audit technology affects decisionmaking in a workpaper review task. The result of an experiment revealed that the electronicenvironment negatively impact auditors‟ performance. Auditors in the electronic workenvironment found to be less able to identify seeded errors and to use them properly inevaluating loan covenants as compared to the auditors in the traditional paper environment. 25
  26. 26. Many studies tested the association between “system use” and “individual impacts” and theassociation was found to be significant in each of the studies. (DeLone and McLean,2003)…to explain one by one..the seven studies2.4 Theoretical backgroundThe previous sections discussed the technology adoption and impact to individualperformance. For the technology to be of value it must be accepted and use. An importantmodel for studying technology adoption and usage is the Unified Theory of Acceptance andUse of Technology (UTAUT).The following sub-sections discuss the history of Technology Acceptance Model (TAM) ofwhich most of the basic variables tested in UTAUT model were adopted partly from thismodel. This is followed by the discussion of the history of UTAUT model, the model tested inthis study. This sub-section lead the discussion into the introduction of the variables adoptedand tested in the current study.2.4.1 The History of Technology Acceptance ModelTechnology Acceptance Models (TAM) have been developed to measure system use,acceptance, and user satisfaction of those systems (Davis, Bagozzi, & Warshaw, 1989). TheDavis model specifically focuses on information systems use and is based on the theory ofreasoned 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 varietyof situations, not just the adoption of information systems technology. Ajzen states that anindividual‟s beliefs influence his/her attitude towards various situations. The users‟ attitudejoins with subjective norms to shape the behavior intentions of each individual. (Cp Moran2006)This theory was further refined and called the theory of planned behavior (TPB) which is alsotitled the extended theory of reasoned action. The TPB is a general behavior model which canbe used to study broader acceptance situations than the TAM but it has been applied toinformation 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. Theseare the users core beliefs about the consequences of the action, the expectations of others, and 26
  27. 27. beliefs about how the user controls, or does not control, the end result of the behavior. Table 1further 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 conceptsof previous….This synthesized model created to present more comprehensive pictures ofacceptance process than any previous model able to do. This model emerged from thecombination of components from eight models previously established in IS literature, all ofwhich had their origins in psychology, sociology and communications. Theses eight modelsare the Theory of Reasoned Action (TRA); Motivational Model (MM); Theory of PlannedBehaviour (TPB); Decomposed Theory of Planned Behaviour (DTPB); TechnologyAcceptance Model (TAM); the Motivational Model (MM); Combined TAM and TPB (C-TAM-TPB); Model of PC Utilization (MPCU); Innovation Diffusion Theory (IDT) ; andSocial Cognitive Theory (SCT). Each model attempts to predict and explain the user behaviorusing a variety of independent variables.Researchers have analysed and compared the competing technology acceptance theories andmodels as noted above in order to identify the most promising ones in respect of the ability topredict and explain individual behaviour towards the acceptance and usage of technology. TheUTAUT model formulated after eight models have been thoroughly revieved and empiricallycompared. The UTAUT model explained about 70 percent of the variance in intention to usetechnology, vastly superior to variance explained by the eight individual model (Rosen,2005). Although the UTAUT model is relatively new, its suitability, validity and reliability intechnology 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 subsequentusage behavior. The main variables tested in the UTAUT model are: performance expectancy,effort expectancy, social influence, and facilitating factors. Venkatesh et al. (2003) explainperformance expectancy as the degree of performance gain after using a new system or atechnology. This is an important variable in predicting user behavior. Considering the fact thatmany people take in-service training courses in order to pursue career enhancementopportunities, it is logical to offer them something new that would contribute to their job 27
  28. 28. performance. Therefore, high performance expectancy can encourage possible users to adoptthe new technology or the new system. Because of its importance, many theories haveadopted this construct in different ways.The second main variable is effort expectancy. This variable measures the degree of effortthat a person needs to put forth when using a new technology or a new system. Research hasshown that users are more likely to adopt or use new technologies if they require a relativelyminimal amount of effort (Agarwal and Prasad, 1997; Konradt et al., 2006). It is likely thatresistance can be expected from the users, when employing a new technology, if the newsystem requires them to work hard in order to learn it. It is a well known fact that manypeople do not resist an innovation itself, but do resist learning a new thing that requires effortinstead of using a well known system. Therefore, this variable is also important in predictinguser behavior in terms of accepting or rejecting a new technology. Effort expectancy groupsseveral constructs fromother theories or models.The third variable in the UTAUT is social influences. Social influences are the external andinternal factors that effect people when making a decision or displaying a behavior. In otherwords, the degree that the people value significant other‟s opinions constitutes socialinfluences. Some people may feel pressure to comply with the proposed behavior, which inthis case can be the use of a new technology, while others may not. Social influence is used asan 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 environmentwith regards to its support for the user. Users may need support such as technical help inusing the new system or the new technology. If the technological environment offers suchsupport, users will be more likely to be in favor of using it. On the other hand, if the systemdoes not offer such support, it would be more difficult to encourage users to adopt the newsystem or technology. Like the previous variables, facilitating conditions is included in earliermodels and theories, but in different formats. One example of this variable in a different 28
  29. 29. format is the perceived behavioral control variable used in TPB. This variable is also used inTAM.This model also incorporates certain variables as moderators of the relationship describedabove. In particular UTAUT model tested several user variables and posits that gender, age,experience, and voluntariness of use (the demographic characteristics), mediate the impact ofthe 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 intechnology adoption. Park, Yang, and Lehto (2007) examined the adoption of mobiletechnologies byconsumers in China. In their study, they surveyed 221 Chinese people in orderto understandtheir perceptions regarding mobile communication technologies. The results oftheir analysisrevealed the role that gender plays in terms of affecting user intentions. Theyfurther revealed that male users were more influenced by performance expectancy thanfemale users. In other words,male users were more focused on increasing their gains frommobile technologies than femaleusers. This finding is consistent with the study by Wang andShih (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 andfocused 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 haveasignificant moderating effect on performance expectancy. On the other hand, both genderandage significantly moderated effort expectancy and social influences. Wang et al. (2009)reportedthat effort expectancy was more important for older users than younger ones. Whilethis findingwas not unexpected, as older users tend to look for less complex systems tooperate, themoderating affect of gender differences on social influences was reallyunanticipated.Interestingly, male users‟ social influences scores were higher than that offemale users; that is,male users were more affected by the opinions of significant others thanfemale users.Age.... 29
  30. 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). Theresearcherstested the University of Oulu‟s SmartRotuuari2 program on 243 people. The resultsindicatedthat experience played a major role in determining user intentions.Experiencepositively moderated performance expectancy and effort expectancy. On the otherhand,facilitating conditions was negatively moderated by experience. Particularly, Koivumakiet 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 alsoproducedsignificant results in terms of moderating variables. According to the results,effort expectancywas greater for old versus young users. Moreover, gender was significant indetermining user intentions. Wang and Shih found that performance expectancy was strongerformen 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. 31. 2.4.2 Computer self-efficacy2.4.2.1 Self-efficacy defineThe concept of self-efficacy is derived from the work of Bandura and Social CognitiveTheory (1986). Social Cognitive Theory (SCT) suggest that human behavior is reciprocallyinfluenced by environmental as well as cognitive factors, which include outcome expectationand self-efficacy (Downey and McMurtrey, 2007). Self-efficacy is an individual‟s confidencein 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 morethan a belief in ability level; it also orchestrates the motivation necessary to conduct thebehavior. Self-efficacy helps determine what activities an individual engages in, the effort inpursuing 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 usingcomputers and a variety of computer behaviors. Compeau and Higgins (1995b) definecomputer 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 completethe specific tasks. They remark on the relative paucity of prior research examining theinfluence 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 withcomputer.This study extends current understanding of the concept of CSE in the context of the usage ofthe audit software . See Argawal et al (2000) pg 419, para 4..explain about CSE concept inaudit software…and it‟s role as moderating factor to training. 31
  32. 32. CHAPTER 3 RESEARCH FRAMEWORK AND HYPOTHESES DEVELOPMENT3.1 IntroductionThe previous chapter has thoroughly reviewed the literature related to UTAUT and individualperformance. This chapter presents a research framework to determine the relationshipbetween research variables. The research variables are: (1) the level of audit softwareapplication as technology adoption constructs (2) performance expectancy, effort expectancy,social influence and facilitating conditions, organizational supports and training as theantecedents of the technology adoption construct (3) experience and computer self-efficacy asa 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 frameworkThe comprehensive review of literature performed in the previous chapter found that most ofprior researches on technology adoption have stopped at the behavioral intention to adoptinformation technology (…..quoted). Review of past studies revealed that only few researcheshave been done on the actual usage and impact of technology, less little on the adoption ofaudit software in audit practices. Several models have been proposed to predict thetechnology adoption such as Technology Acceptance Model (Davis et al. 1989), Theory ofPlanned 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 itis used (Dowling, 2009). A number of studies attempted to extend the technology acceptancestudy further into the use of technology in audit practice. For example, Bierstaker, Burnaby,& Thibodeau, (2001) assessed thecurrent impact of technology on the auditprocess, and thefuture implicationsof technological trends for the auditingprofession.Based on the reviews done on the several technology adoption model as well as mid-rangetheories related to the technology adoption, the theoretical foundation of this study ispremised 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. 33. outcome of the technology adoption. Until today, little to no research has addressed the linkbetween user acceptance and individual performance outcome. Thus, the assumption thattechnology 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 audittechnology.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- EfficacyHere need to explain about the adoption of audit software in auditing practices. Need to proofthat there is no attempt to investigate the adoption and impact to performance as well as thedeterminat factors.. 33
  34. 34. 3.3 Operationalization and measurement of variablesThe present study is based on the UTAUT model which was developed and tested byVenkatesh (2003). The focus of the study is to examine the factors that influence theapplication of audit software amongst the auditors in different audit firms that adopting auditsoftware in practice. This study is also aimed to examine the impact of the audit softwareapplication to the individual audit performance. As mentioned earlier (make sure this has beenmentioned), UTAUT model is in principle open for inclusion of other predictors if thesepredictors can explain significant variance in the technology adoption,in an attempt toprovide an even richer understanding of technology adoption and usage behavior. Giveexample other studies that recommended other variables..As stated previously, the UTAUT model is open for inclusion (make sure this is mentionedpreviously) of other variables pertinent to behavior usage of audit software. Therefore,besides the main variables tested in UTAUT model, the conceptual framework of the presentstudy also includes computer self-efficacy (Compeau and Higgins,1995; Burkhardt andBrass, 1990; Gist et al., 1989 ), training factorsas one of the elements influence the usagebehavior, elements, the present study also includes additional variables that can explain more .The main variables introduced in UTAUT model are performance expectancy, effortexpectancy, social influence and facilitating condition. The study introduced two newvariables to the existing UTAUT model; client technology and computer self-efficacy. Thisstudy tested the moderating effect of experience to the relationship of performance and effortexpectancy to the audit software application as tested in UTAUT model. To consider thecontribution of this study, new moderating variable was also tested to see the interactioneffect of training to the relationship of performance and effort expectancy to the auditsoftware application as tested in UTAUT model.3.3.1 Audit software applicationThis study used the term audit software application to describe software used to assist auditorsin completing one or more tasks. Reviewed of prior literature and discussion held withpractitioners 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 theaudit applications 34
  35. 35. 3.3.2 Determinant factors of audit software adoption.3.3.3 Individual performanceThe choice of performance measures is one of the most critical challenging facingorganizations. Performance measurement systems play a key role in developing strategicplans, evaluating the achievement of organizational objectives, and compensating managers(Ittner and Larcker, 1998). The individual performance is the ultimate dependent variable orthe criterion variable in this present research. Generally, individual performance is intended toidentify the extent to which users believe that adopting technology in performing the taskgives impact to their job performance. Individual performance also has been examined in itsspecific aspects such as use of new technology enrich the work …………………In thispresent research, individual performance is defined as the extent to which auditors believethat using audit software in performing their audit task can increase the performance of auditworks.Individual performance has been measured in audit technology context by several researchers.For example ……3.4 Hypotheses DevelopmentAccording to Vierra, Pollack and Golez (1998) researchers normally restate researchquestions as hypotheses because hypotheses can be subjected to empirical testing. This meansthey can be tested using some form of research procedure such as observations or surveys. Inthis 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 theobservation of past literatures according to the richness of measurement. When there aremixed results from literature, this study consider the majority results to develop thehypothesis.Generally, the relationships investigated in the present research and their related hypothesescan be classified as: (1) between audit software application and audit performance, (2)between the determinant factors and audit software application, and (3) between thedeterminants factors and audit software application with the interaction of moderatorvariables. Details of the above are discussed in the following subsections. 35
  36. 36. 3.4.1 Audit software application and audit performanceResults of previous studies that have tested the IT use and individual performance relationshipshown mixed results. McGill, Hobbs, & Klobas (2003) and Livari (2005) did not findsignificant relationship between IT use and individual performance. Both of them usedfrequency as the IT use measure. For the performance measurements, McGill et al., (2003)measured the subjective effectiveness, productivity, and performance of user-developdapplication. Livari (2005) measured the perceived efficiency, productivity nad effectivenessof a financial accounting system.3.4.2 Determinant factors of audit software useAlthough UTAUT is quite a new theory, dating to 2003, the literature reviewrevealedhundreds of research studies that use UTAUT as a theoretical background. This is anindicator ofthe high degree of acceptance of UTAUT by scholars from many disciplines. Inthe followingsection, some of these studies are detailed, providing information regardingvariables used in themodel and their level of significance in the respective studies. (cpodabasi, 2010)3.4.2.1 Performance ExpectancyThis variable is considered to be the most important one in the UTAUT model. Theperformance expectancy variable in the UTAUT predicts a positive relationship betweenintention to use technology and gains in job performance. Actually,in most user acceptancestudies, performance-related variables such as perceived usefulnessattract the most attention.In order to determine whether this variable is indeed the mostimportant variable, results fromstudies in different disciplines are listed below.Anderson et al. (2006) applied the UTAUTmodel to understanding the perceptions ofuniversity faculty toward tablet personal computer(PC) usage. They surveyed 50 facultymembers by using web-based survey methods. As aresult of their study, Anderson et al. Foundthat performance expectancy was the “strongestpredictor” (Anderson et al. 2006, p.430). According to theirstudy, performance expectancypositively affected the usage of the tablet PC. In other words, thefaculty who believed thatusing a tablet PC increased their work performance tended to use thetablet PC more than thefaculty who thought otherwise.Performance expectancy produced similar results in Wang and Shih (2009) studyofinformation kiosk systems. They explored the perceptions of 244Taiwanese users in their 36
  37. 37. use of an E-Government information kiosk. Performance expectancy was operationalised asthe increased gain in accessing government relatedinformation and concluded that theirintention to use the information kiosks was heavilyinfluenced by their level of performanceexpectancy. Therefore, increasing theperformance expectancy level of the users guaranteed ahigh usage of the E-government kiosks.In addition to the E-Government and academicenvironments, performance expectancywas also found to be the most influential factor inadopting 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 performanceexpectancy with greatereconomic benefits, such as increased customer contact andimprovement of business processes.They assumed that a system which increases the ability ofa company to contact buyers andsellers would be acceptable to that company. Furthermore, ifthe 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 businesssector to use EM. In otherwords, the results of their study confirmed the significant affect ofperformance expectancy onthe intention to use the EM.In another study, Bandyopadhyay and Fraccastoro (2007) used the UTAUT model in ordertounderstand the perceptions of users towards prepayment metering systems. Theresearchershypothesized that consumers would prefer to use the prepayment meter technologyovertraditional payment methods, if they believe it is a useful system for managing theirelectricityusage. The results of the study confirmed this hypothesis in the finding of asignificantrelationship between performance expectancy and the intention to use the system. Inotherwords, people who thought that using the prepayment metering system would be helpfulinelectricity account management intended to use the system more than people whothoughtotherwise. Furthermore, like many other scholars, Bandyopadhyay and Fraccastoro(2007) determinedthat performance expectancy was the strongest variable within thetheoretical 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 ; auditplanning, testing ad documenttaionHence, the following is hypothesised: 37
  38. 38. H2 : Auditors with high performance expectancy are associated with high usage of auditsoftware.3.4.2.2Effort ExpectancyLike performance expectancy, effort expectancy is considered to be an importantdeterminantof user intentions. In the user acceptance literature, most of the studies found asignificantrelationship between effort expectancy and intention. However, the relationship wasnot asstrong as with performance expectancy.Lin and Anol (2008) studied online social support andnetwork IT usage among 317Taiwan university students by employing the UTAUT. Theyoperationalized effort expectancy asthe degree of easiness in using the network IT. Ananalysis of the survey revealed a significantrelationship between effort expectancy and userintentions. Students who found the system easyto use were more likely to use the system thanthose who found the system difficult to use.Wu, Tao, and Yang (2007) also used UTAUT as a theoretical background in theirresearchstudy. They studied the perceptions of users of 3G mobile communication systemsandhypothesized that effort expectancy would play a major role in increasing the intentionscores ofthe users. The researchers surveyed 394 users, by using an online questionnaire.Using structuralequation modeling, Wu et al. found a significant relationship between effortexpectancy and theintention to use 3G mobile technologies. Therefore, they demonstratedtheir hypothesis abouteffort expectancy boosting user intentions to be correct.Im, Hong, and Kang (2007) studied mp3 player and internet banking technologies intwodifferent settings, namely Korea and the United States. They wanted to comparetheperceptions of users from both countries to see whether there were any differences. Im etal. (2007) collected data from 501 users including Korean college students and USundergraduate students.They defined effort expectancy as the easiness of using the mp3players and internet banking.Notwithstanding the differences in nationality, the resultsdemonstrated a significant relationshipbetween effort expectancy and user intentions for bothKorean and US students. Users whofound using both the mp3 players and Internet bankingeasy had high intention scores.Although a majority of the studies demonstrate a significantrelationship between effortexpectancy and user intentions, there are a limited number ofstudies that show otherwise.Anderson, Schwager, and Kerns (2006) studied the perceptions of 38
  39. 39. college faculty in their use of tablet PCs. They hypothesized that the ease of use of the tabletPCs would positively affect userintentions. In other words, they expected to see higherintention scores from users who found thetablet PC easy to operate. However, the study didnot produce any significant results in terms ofeffort expectancy, and their hypothesis wasrejected.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 InfluencesMarchewka et al. (2007) examined the Blackboard application which is a type ofeducationalsoftware widely used by the university community. Their sampling frame wasuniversitystudents both at the graduate and undergraduate levels. After surveying 132universitystudents, they concluded that there was a significant relationship between socialinfluences andintention to use the Blackboard system. According to the results, students areaffected by theirsignificant others‟ opinions in terms of their use of the Blackboard system. Ifthey 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.Shehypothesized that social influence scores would positively affect users‟ intention to usetheVOIP systems. In other words, users would decide whether to use the system based ontheopinions of people whom they consider important. Armida surveyed 475 respondentsfromvarious states in order to conduct her study. After statistical analysis, Armida concludedthatsocial influences were a significant predictor of intention to use the VOIP systems.Neufeld, Dong, and Higgins investigated the relationship between charismatic leadershipandthe adoption of information technology (2007). Neufeld et al. collected a sample of207respondents from 7 organizations. and hypothesized that social influence was adeterminant of ITadoption. An analysis of their data resulted in positive scores for socialinfluences. In otherwords, the results supported their hypothesis and found a significant relationship betweensocialinfluences 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

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