Nowadays, in the context of the rapid advancement in the IT field, radical changes have taken place in audit and accounting practices. Despite the recent increased attention from practitioners and scholars on Computer-Assisted Audit Tools and Techniques (CAATT) usage, there is still a very limited number of empirical studies related to this usage in developing countries like Sudan.
Objective
This paper focuses on the user-related antecedents and perceived service quality outcomes of CAATT usage within selected auditing organizations in Sudan. Based on this, a model has been proposed and tested to investigate user-related antecedents that influence CAATT usage and its relationship with perceived audit quality.
Method
Data was collected from 131 auditors using purposive sampling and analysis was done by applying partial least squares structural equation model analysis (PLS-SEM).
Results
The results showed that six out of nine hypothesized relationships significantly influence CAATTs usage. In contrast, three user antecedents showed no significant influence, while perceived service quality was found to be significantly influenced by CAATTs usage.
Conclusion
The study has filled the existing research gap by studying the user-related antecedents in CAATT usage within the context of developing countries. The research findings indicate that more research is required to enable wider CAATTs diffusion to enhance perceived audit quality.
AsnaashariHSafarzadehMHKheirollahiA, et al.The effect of auditors’ work stress and client participation on audit quality in the COVID-19 era. J Facil Manag2023; 23(2): 181–208.
2.
SiewEGRosliKYeowPHP. Organizational and environmental influences in the adoption of computer-assisted audit tools and techniques (CAATTs) by audit firms in Malaysia. Int J Account Inf Syst2020; 36: 100445.
3.
AlshurafatHShbailMOAAlmuietM. Factors affecting the intention to adopt IT forensic accounting tools to detect financial cybercrimes. Int J Bus Excel2024; 33(2): 169–190.
4.
AlrahamnehS. Enhancing internal audit quality in Jordanian insurance companies a coso framework perspective. Edpacs2024; 69(6): 1–27.
5.
ІВО. Role of document management system for business processes optimization. Проблеми теорії та методології бухгалтерського обліку. контролю і аналізу2024; 2(58): 45–49.
6.
HajdariBMlinakuH. Application of information technology in human resource management. Eurasia Proc Sci Technol Eng Math2023; 26(1): 780–796.
7.
PearlsonKESaundersCS. Managing and using information systems: a strategic approach. In: Journal of chemical information and modeling. John Wiley & Sons, 2010, p. 401.
8.
AsmaahS. Effects of computer assisted audit techniques on quality of internal audit reports in Kenya. Afr J Commer Stud2023; 3(1): 34–45.
9.
MahdMIMoghadamMBAbousaeidiM. Presenting the acceptance model of computer-based auditing tools and techniques with the ISM technique. Int J Nonlinear Anal Appl [Internet]2024; 15(12): 2008–6822, Available from. https://dx/
10.
MujalliA. The influence of E-auditing adoption on internal audit department performance amid COVID-19 in Saudi Arabia. Cogent Bus Manag2024; 11(1): 2295608.
11.
AlOHMAKadirMRASapingiRB, et al.The dynamics of CAATs adoption in Jordan: bridging top management support with auditor innovativeness and IT competency. WSEAS Trans Bus Econ2025; 22: 20–31.
12.
AbeysiriMLPerumbuli MudaligeDM. Audit software (CAAT) andAudit quality: a qualitative study regarding impactaudit software and audit quality. SriLankan context, 2024.
13.
Adorm-TakyiC. Factors influencing audit quality of audit firms in Ghana: the role of audit automation adoption, corporate culture, and technological training. University of Cape Coast, 2023.
14.
Thottoli MMKVT. Characteristics of information communication technology and audit practices: evidence from India. VINE J Inf Knowl Manag Syst2022; 52(4): 570–593.
15.
SalijeniGSamsonova-TaddeiATurleyS. Understanding how big data technologies reconfigure the nature and organization of financial statement audits: a sociomaterial analysis. Eur Account Rev2021; 30(3): 531–555.
16.
ElmashtawyAChe HaatMHIsmailS, et al.Audit committee effectiveness and audit quality: the moderating effect of joint audit. Arab Gulf J Sci Res2023; 42(3): 512–533.
17.
AlwadieA. Impact of technology on auditing: evidence in developing countries. Int J Sci Res2024; 3(2): 29–48.
18.
AljabriMAGAl-AnsiAA. IT audit acceptance among external auditors in Saudi Arabia: mediating role of it audit importance. In: Studies in Systems, Decision and Control. Springer, 2024, pp. 1053–1076.
19.
AlmagrashiAMujalliAKhanT, et al.Factors determining internal auditors’ behavioral intention to use computer-assisted auditing techniques: an extension of the UTAUT model and an empirical study. Futur Bus J2023; 9(1): 74.
20.
SiklóssyL. On the evolution of artificial intelligence. Inf Sci1970; 2(4): 369–377.
21.
BoczkoT. Accounting information systems audit. In: Information Systems in Accounting and Finance. Springer, 2024, pp. 477–526.
22.
SihemBAhmedBAlzoubiHM, et al.Effect of big data analytics on internal audit case: credit suisse. In: 2nd International Conference on Business Analytics for Technology and Security, ICBATS 2023, Dubai, 7–8 March 2023, pp. 1–11. IEEE.
23.
OladeindeMOkelekeECRhoda AdaramoduO, et al.Communicating it audit findings: strategies for effective stakeholder engagement. Comput Sci IT Res J2023; 4(2): 126–139.
24.
BepariMKNaharSMollikAT. Perceived effects of key audit matters reporting on audit efforts, audit fees, audit quality, and audit report transparency: stakeholders’ perspectives. Qual Res Account Manag2024; 21(2): 192–218.
25.
LiniTZ. Key success factors and audit maturity level evaluation in organizations from continuous auditing perspective [internet]. University of Twente, 2023. Available from. https://essay.utwente.nl/96657/1/ZataLini_MA_EEMCS.pdf
26.
TheodorakopoulosLThanasasGHalkiopoulosC. Implications of big data in accounting: challenges and opportunities. Emerg Sci J2024; 8(3): 1201–1214.
27.
SupriadiI. The audit revolution: integrating artificial intelligence in detecting accounting fraud. Akunt dan Teknol Inf2024; 17(1): 48–61.
28.
PatelSShahM. A comprehensive study on implementing big data in the auditing industry. Ann Data Sci2023; 10(3): 657–677.
29.
SeethamrajuRHecimovicA. Impact of artificial intelligence on auditing - an exploratory study. In: 26th Americas conference on information systems, AMCIS 2020, Utah, USA, 10–14 August 2020, Dublin Business School.
30.
SaudIMSofyaniHUtamiTP, et al.Big data analytics-based auditing adoption in public sector: indonesian evidence. Cogent Bus Manag2025; 12(1): 2454320.
31.
ŞenY. Auditors ’ Perception S of Artificial Intelligence : evidence From Global Audit Companies Auditors ’ perception S of artificial intelligence. In: Evidence from global audit companies. İzmir Ekonomi Üniversitesi, 2023.
32.
DaoudL. Predictors of auditors’ usage of CAATs: the role of top management support and trust. Inf Sci Lett2023; 12(5): 1841–1850.
33.
WijerathnaJ. The impact of organizational factors and environmental factors on the adoption of computer assisted audit tools and techniques (CAATTs) by SME in Sri Lanka. Wayamba J Mgt2024; 15(1): 1–22.
34.
EissaOMS. A comprehensive model for factors affecting the usage of computer-assisted auditing tools and techniques. Bus and Manag Rev2023; 14: 224–233.
35.
SenanNAM. Influential factors shaping the adoption and utilization of audit technology (CAATS) in the audit practices in Saudi Arabia: human capital expertise as a moderating factor. Uncertain Supply chain Manag2024; 12(4): 2607–2618.
36.
EzeonwukaAJLe RouxSOgbodoC. The efficiency of adopting generalized audit software in internal auditing: a case study of internal auditors in Nigeria. Int J Appl Res Bus Manag2024; 5(1): 62–78.
37.
AsmahAKyobeM. The impact of audit on IT governance: a study of the financial services sector in Ghana. E J Info Sys Dev Countries2025; 91(1): e12349.
38.
IwuanyanwuUApehAJAdaramoduOR, et al.Analyzing the role of artificial intelligence in it audit: current practices and future prospects. Comput Sci IT Res J2023; 4(2): 54–68.
39.
LiuMWuKXuJJ. How will blockchain technology impact auditing and accounting: permissionless versus permissioned blockchain. Curr Issues Audit2019; 13(2): A19–A29.
40.
FelicianoCQuickR. Innovative information technology in auditing: auditors’ perceptions of future importance and current auditor expertise. Account Eur2022; 19(2): 311–331.
41.
ThottoliMMAhmedERThomasKV. Emerging technology and auditing practice: analysis for future directions. Eur J Manag Stud2022; 27(1): 99–119.
42.
AlqudahHMansourAZRawashdehBS, et al.Enhancing the internal auditors’ effectiveness in Jordanian companies: the impact of cloud-based accounting usage and the moderating role of digital proficiency. Comput Hum Behav Rep2024; 15: 100442.
43.
AmoakoGKBawuahJAsafo-AdjeiE, et al.Internal audit functions and sustainability audits: insights from manufacturing firms. Cogent Bus Manag2023; 10(1): 2192313.
44.
KamaraAK. The study on effectiveness of internal audit on the performance of the public sector in Sierra Leone: a case study of the National Social Security Insurance Trust. OAlib2023; 10(8): 1–30.
45.
KokinaJBlanchetteSDavenportTH, et al.Challenges and opportunities for artificial intelligence in auditing: evidence from the field. Int J Account Inf Syst2025; 56: 100734.
46.
MökanderJ. Auditing of AI: legal, ethical and technical approaches. Digit Soc2023; 2(3): 49.
47.
TümmlerMQuickR. How to detect fraud in an audit: a systematic review of experimental literature. Manag Rev Q2025; 1–46.
48.
AppelbaumDShowalterDSSunT, et al.A framework for auditor data literacy: a normative position. Account Horiz2021; 35(2): 5–25.
49.
DuttaARoyRSeetharamanP. An assimilation maturity model for IT governance and auditing. Inf Manag2022; 59(1): 103569.
50.
RuhnkeK. Empirical research frameworks in a changing world: the case of audit data analytics. J Int Accounting, Audit Tax2023; 51: 100545.
51.
ThottoliMM. Leveraging information communication technology (ICT) and artificial intelligence (AI) to enhance auditing practices. Account Res J2024; 37(2): 134–150.
52.
SantosoB. The impact of information technology on the audit process: auditors’ perspectives. Golden Ratio Audit Res2021; 1(1): 11–23.
53.
LorentzonJIFotohLEMugwiraT. Remote auditing and its impacts on auditors’ work and work-life balance: auditors’ perceptions and implications. Account Res J2024; 37(1): 1–18.
54.
ChristMHEulerichMKraneR, et al.New frontiers for internal audit research. Account Perspect2021; 20(4): 449–475.
55.
MustikaJKarlinaLTirtaBD. Using the UTAUT model to analyze perception of independent auditor on usage of computer assisted audit techniques. In: Medan International Conference Economics and Business, Sumatera, Utara, 12–13 November 2023, pp. 629–637.
56.
DamerNAl-ZnaimatAHAsadM, et al.Analysis of motivational factors that influence usage of computer assisted audit techniques (CAATS) by external auditors in Jordan. Acad Strateg Manag J2021; 20(SpecialIssue2): 1–13.
57.
MohammedHARabiuNBBardeIM. Determinants of adoption of computer assisted auditing techniques: a survey of auditors in Kano state, Nigeria. FUDMA J Account Financ Res [FUJAFR]2023; 1(3): 146–161.
58.
AchmadTHuangCYPutraMA, et al.Forensic accounting and risk management: exploring the impact of generalized audit software and whistleblowing systems on fraud detection in Indonesia. J Risk Financ Manag2024; 17(12): 573.
59.
JacksonDAllenC. Enablers, barriers and strategies for adopting new technology in accounting. Int J Account Inf Syst2024; 52: 100666.
60.
VitaliSGiulianiM. Emerging digital technologies and auditing firms: opportunities and challenges. Int J Account Inf Syst2024; 53: 100676.
61.
HezamYAAAnthonysamyLSuppiahSDK. Big data analytics and auditing: a review and synthesis of literature. Emerg Sci J2023; 7(2): 629–642.
62.
KriegerFDrewsPVelteP. Explaining the (non-) adoption of advanced data analytics in auditing: a process theory. Int J Account Inf Syst2021; 41: 100511.
63.
NguyenTTNguyenLAKendM, et al.The challenges facing Vietnamese accountants and auditors with the adoption of emerging technologies. In: Handbook of Big Data and Analytics in Accounting and Auditing. Springer, 2023, pp. 41–63.
64.
KaurR. Caatts adoption in Malaysian big 4 audit firms. University of Malaya, 2024.
65.
Al-HiyariAAl SaidNHattabE. Factors that influence the use of computer assisted audit techniques (caats) by internal auditors in Jordan. Acad Account Financ Stud J2019; 23(3): 1–15.
66.
StackRMalschB. Auditors’ professional identities: review and future directions. Account Perspect2022; 21(2): 177–206.
FaqihKMSJaradatMIRM. Integrating TTF and UTAUT2 theories to investigate the adoption of augmented reality technology in education: perspective from a developing country. Technol Soc2021; 67: 101787.
69.
DavidAYigitcanlarTLiRYM, et al.Understanding local government digital technology adoption strategies: a PRISMA review. Sustain Times2023; 15(12): 9645.
70.
FerriLSpanòRGinestiG, et al.Ascertaining auditors’ intentions to use block chain technology: evidence from the big 4 accountancy firms in Italy. Meditari Account Res2021; 29(5): 1063–1087.
71.
AwuahBOnumahJMDuhoKCT. Determinants of adoption of computer-assisted audit tools and techniques among internal audit units in Ghana. E J Info Sys Dev Countries2022; 88(2): e12203.
72.
SeethamrajuRHecimovicA. Adoption of artificial intelligence in auditing: an exploratory study. Aust J Manag2023; 48(4): 780–800.
73.
MthimunyeMP. The impact of emerging technologies on internal audit functions. University of Johannesburg (South Africa), 2023.
74.
RzayevaA. Process views in auditing research. A structured literature review. 2020.
75.
AllamiKKJAlmaqtariFAAl-HattamiHM, et al.Factors associated with the intention to use information technology in audit in Iraq. Inf Discov Deliv2024; 52(2): 197–212.
76.
AttaAABBaniataHMOthmanOH, et al.The impact of computer assisted auditing techniques in the audit process: an assessment of performance and effort expectancy. Int J Data Net Sci2024; 8(2): 977–988.
77.
DavisFD. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q Manag Inf Syst1989; 13(3): 319–339.
78.
PhamTLTranTPTDauTKT, et al.The determinants of digital competencies in accounting: an empirical study in Vietnam. High Educ Ski Work Learn2024; 15(2).
79.
PurnamasariPAmranNAHartantoR. Modelling computer assisted audit techniques (CAATs) in enhancing the Indonesian public sector. F1000Res2022; 11: 539–559.
80.
LaiP. The literature review of technology adoption models and theories for the novelty technology. J Inf Syst Technol Manag2017; 14(1): 21–38.
81.
GodefroidMEPlattfautRNiehavesB. Identifying key barriers to nonprofit organizations’ adoption of technology innovations. Nonprofit Manag Leadersh2023; 35(1): 237–259.
Samson AyinlaBAtadogaAIkeCU, et al.The role of robotic process automation (Rpa) in modern accounting: a review - investigating how automation tools are transforming traditional accounting practices. Eng Sci Technol J2024; 5(2): 427–447.
84.
ParkIKimDMoonJ, et al.Searching for new technology acceptance model under social context: analyzing the determinants of acceptance of intelligent information technology in digital transformation and implications for the requisites of digital sustainability. Sustain Times2022; 14(1): 579.
85.
BalakrishnanJDwivediYKHughesL, et al.Enablers and inhibitors of AI-Powered voice assistants: a dual-factor approach by integrating the status quo bias and technology acceptance model. Inf Syst Front2024; 26(3): 921–942.
86.
MagboulIJebreelMShawtriF, et al.Factors influencing accounting information system usage by oil companies & performance outcomes. Cogent Bus Manag2024; 11(1): 2369211.
87.
TarekMMohamedEKAHussainMM, et al.The implication of information technology on the audit profession in developing country: extent of use and perceived importance. Int J Account Inf Manag2017; 25(2): 237–255.
88.
OwinoBAMusuvaPMW. Empirical assessment of improved audit quality factors using computer-assisted audit tools and techniques (caatts). Spec Issue Technol Soc Collab with Google Africa2021; 12(2): 2021.
89.
AhmadWNWZakariaM. Understanding the transformative potential of computer-assisted audit tools and techniques (CAATTs) in the accounting profession. In: Proceedings of International Conference on Economics Business and Government Challenges, Indonesia, 11 September 2023, pp. 16–20.
90.
PodsakoffPMMacKenzieSBPodsakoffNP. Sources of method bias in social science research and recommendations on how to control it. Annu Rev Psychol2012; 63: 539–569.
91.
KrejcieRVMorganDW. Determining sample size for research activities. Educ Psychol Meas1970; 30(3): 607–610.
92.
HandokoBLAriyantoSWarganegaraDL. Perception of financial auditor on usage of computer assisted audit techniques. In: Proceedings - 3rd International Conference on Computational Intelligence and Applications, ICCIA 2018, Hong Kong, 28–30 July 2018, pp. 235–239. IEEE.
93.
VasarhelyiMAAllesMKuenkaikaewS, et al.The acceptance and adoption of continuous auditing by internal auditors: a micro analysis. Int J Account Inf Syst2012; 13(3): 267–281.
94.
Al-HaddadHBGalibMHHerzallahFAT. Consumer intention toward participation in proximity marketing. Int J E-Services Mob Appl2024; 16(1): 1–18.
95.
TsaiCCLinCLChenYH. Impact of authenticity perception on experiential value and customer satisfaction under contactless services. Systems2024; 12(1): 19.
96.
VenkateshVMorrisMGDavisGB, et al.User acceptance of information technology: toward a unified view. MIS Q Manag Inf Syst2003; 27(3): 425–478.
97.
SarstedtMRingleCMHairJF. Partial least squares structural equation modeling. In: Handbook of market research. Springer International Publishing, 2021, pp. 587–632. (Classroom Companion: Business).
98.
HairJFRisherJJSarstedtM, et al.When to use and how to report the results of PLS-SEM. Eur Bus Rev2019; 31: 2–24.
99.
KetchenDJ. A primer on partial least squares structural equation modeling. Long Range Plann2013; 46(1–2): 184–185.
100.
HairJFHowardMCNitzlC. Assessing measurement model quality in PLS-SEM using confirmatory composite analysis. J Bus Res2020; 109: 101–110.
101.
BagozziRPYiY. Specification, evaluation, and interpretation of structural equation models. Vol. 40. J Acad Mark Sci2012; 40: 8–34.
102.
HenselerJRingleCMSarstedtM. Testing measurement invariance of composites using partial least squares. Int Mark Rev2016; 33(3): 405–431.
103.
HenselerJRingleCMSarstedtM. A new criterion for assessing discriminant validity in variance-based structural equation modeling. J Acad Mark Sci2015; 43(1): 115–135.
104.
AlnsourIRAlghadiMYAhmadAB, et al.Islamic financial technology acceptance: an empirical study in Jordan. Int J Data Net Sci2023; 7(4): 1659–1668.
105.
LutfiAAlqudahH. The influence of technological factors on the computer-assisted audit tools and techniques usage during COVID-19. Sustain Times2023; 15(9): 7704.
106.
NasrahHMudaIKesumaSA. Computer assisted audit tools and techniques adoption: a systematic literature review. Int J Soc Serv Res2023; 3(3): 630–638.
107.
SamagaioADiogoTA. Effect of computer assisted audit tools on corporate sustainability. Sustain Times2022; 14(2): 705.
108.
OmonukJBOniAA. Computer assisted audit techniques and audit quality in developing countries: evidence from Nigeria. J Internet Bank Commer2015; 20(3): 1.
109.
van der NestDPSmidtLLubbeD. The use of generalised audit software by internal audit functions in a developing country: a maturity level assessment. Risk Gov Control Financ Mark Institutions. 2018;7(4–2):189–202.
110.
AksoyTGurolB. Artificial intelligence in computer-aided auditing techniques and technologies (CAATTs) and an application proposal for auditors. In: Contributions to Finance and Accounting. Springer, 2021, pp. 361–384.
111.
SorenAAChakrabortyS. Adoption, satisfaction, trust, and commitment of over-the-top platforms: an integrated approach. J Retail Consum Serv2024; 76: 103574.
112.
GranićA. Technology adoption at individual level: toward an integrated overview. Univers Access Inf Soc2024; 23(2): 843–858.
113.
KoesterNCichyPAntonsD, et al.Perceived privacy risk in the internet of things: determinants, consequences, and contingencies in the case of connected cars. Electron Mark2022; 32(4): 2333–2355.
114.
ShihabMRMeilatinovaNHidayantoAN, et al.Herkules. Determinants of CAATT acceptance: insights from public accounting firms in Indonesia. Procedia Comput Sci2017; 124: 522–529.
