Jun 2026· Journal of Accounting and Management Information Systems· Vol 25, pp. 166-202· 0 citations· 53 references
TL;DR
The sociomaterial lens allows us to observe that the auditor’s reconfiguration occurs dynamically and continuously, relying both on the evolution of technological capabilities (material agency) and on professionals’ engagement and adaptation (social agency).
Abstract
Research Question: What are the drivers and inhibitors of Artificial Intelligence (AI) use in auditing, and how does AI reconfigure the auditor’s role?
Motivation: The adoption of Artificial Intelligence (AI) in auditing has advanced rapidly, transforming processes, resources, and professional practices.
Idea: The analysis is grounded in sociomateriality theory and examines how the introduction of AI reconfigures the auditor’s role, posing new challenges.
Data: The study is based on a Systematic Literature Review (SLR) of 43 studies.
Tools: The sociomaterial lens is used to analyze the interaction between auditors and AI tools, considering both technological capabilities and professionals’ engagement and adaptation.
Findings: The results indicate that AI adoption in auditing is driven by efficiency, accuracy, real-time auditing, Big Data analytics and standardization. However, barriers such as resistance to change, algorithm aversion, heuristics and biases, transparency, expertise and training gaps, and complexity limit the full adoption of these technologies. This process is dynamic and ongoing: as technology evolves, organizational practices and arrangements also transform, rebalancing functions and responsibilities.
Contribution: From this perspective, the benefits of AI in auditing can be more effectively realized when organizational practices support interaction between auditors and AI tools. Therefore, the sociomaterial lens allows us to observe that the auditor’s reconfiguration occurs dynamically and continuously, relying both on the evolution of technological capabilities (material agency) and on professionals’ engagement and adaptation (social agency).
The digital transformation of the auditing profession is
accelerating as firms adopt technologies such as artificial
intelligence, blockchain, big data analytics, and robotic
process automation. While a growing body of international
literature documents the benefits and risks of these tools,
there is a lack of knowledge about auditors’ perceptions in
the Middle East. This study responds to that gap by
examining how Lebanese auditors perceive the benefits,
challenges, costs, and impacts of emerging technologies.
Building on the Technology Acceptance Model and recent
audit innovation literature, hypotheses were developed
that perceived usefulness (benefits) and ease of use
(captured through perceived costs and challenges)
influence auditors’ intention to adopt digital tools. A cross
sectional survey was distributed to Lebanese external
auditors. Eighty-four responses were analyzed using
descriptive statistics and Mann-Whitney U tests to
compare perceptions between technology users and non-
users. Results indicate that big data analytics is the most
widely adopted technology among Lebanese auditors.
Specifically, users of big data analytics reported
significantly higher median scores for perceived benefits
(e.g., improved audit quality, the ability to analyze
complete data sets, and real-time auditing) compared to
non-users. Its users also report significantly higher
benefits (e.g., improved audit quality, the ability to analyze
complete data sets, and real-time auditing) and impacts
on their work compared with non-users. Conversely,
adoption of artificial intelligence, blockchain, robotic
process automation, and metaverse tools remains limited,
and no significant differences were found between their
users and non-users.Across all technologies, auditors expressed high levels of
concern about cyber security, skills shortages, legal
uncertainties, and start up costs were perceived as high.
The findings contribute to audit technology literature by
providing evidence from a developing country context and
by extending Technology Acceptance Model to
incorporate perceived cost and risk factors. Practical
implications for regulators and practitioners include the
need for targeted training programs, supportive regulatory frameworks, and incentives to encourage investment in digital tools. Directions for future research are also
discussed.
Walaa Khoder Kattar, Mehmet Nuri Salur· Audit Financiar· 0 citations
This study explores auditors’ trust in AI in a limited-resource and turbulent context, namely Lebanon. Using semi-structured interviews with 14 junior and senior auditors in Lebanon, this study identifies the factors that make auditors either willing or unwilling to trust in AI-powered audit processes. The results indicate that auditors perceive the potential benefits of AI, such as increased speed, capacity, and ability to detect anomalies. Yet, trust in AI is limited by concerns about implementation costs, the erosion of professional judgment, blockchain complexity, data overload, and reliance on high-quality data. Instead of excluding AI from the equation, auditors perceive it as tolerable only in conjunction with the role of a person who provides control and supervision, transparent and explainable output, proper management of input data, and established accountability processes. This study addresses the gap in the literature regarding AI auditing by focusing on the practical conditions for building trust in uncertain audit settings
Joseph Serghani· Arab Economic and Business J...· 0 citations
In the context of Vietnam’s efforts to promote science, technology, innovation, and national digital transformation, the adoption of artificial intelligence (AI) in auditing is becoming an inevitable trend. AI offers significant benefits to the auditing profession by improving audit efficiency, enhancing risk assessment, and supporting the analysis of large volumes of data. However, the successful implementation of AI depends not only on technological capabilities but also on auditors’ readiness to accept and use the technology. Based on the Unified Theory of Acceptance and Use of Technology (UTAUT), this study proposes a theoretical framework to explain auditors’ intention to use AI in Vietnam. Specifically, willingness to learn AI (WLA) is proposed as a mediating mechanism through which performance expectancy (PE), effort expectancy (EE), and social influence (SI) affect intention to use AI. In addition, organizational support (OS) is proposed as a moderating factor that strengthens the relationship between WLA and intention to use AI. The proposed model provides a foundation for future empirical research and contributes to the understanding of AI adoption behavior among auditors in emerging economies.
Linh-Giang Le Nguyen· Journal of Economics, Busine...· 0 citations
Following the International Auditing and Assurance Standards Board’s (IAASB) findings, artificial intelligence (AI) developments over human governance in the February 2026 Technology Quality Management roundtables outcome statement, this study aims to disclose the disconnect between the policy and practice of external audit functions in AI adoption. The research employs a qualitative multi-method design that compares the narratives of Big Four organizations’ transparency reports and audit practitioners in the United Arab Emirates (UAE) and Egypt. The study’s context was determined by the identified gaps in prior empirical research on the differences between the attitudes of corporate management and auditors towards AI usage in external audits. The lead research question focuses on the distinctions in Big Four strategies and individual auditors’ practices in AI applications. The findings are based on content analysis of the narrative of Big Four organizations’ 2021–2025 transparency reports and thematic analysis (TA) of the semi-structured interviews with the auditors in the UAE and Egypt in 2026. The study discovers that auditor practices are currently disconnected from the strategic level propositions. While corporate reports depict a vision in which AI is regarded as a normal component of audit processes, auditors’ experiences suggest it is limited and token adoption in practice. The authors conclude that governance and transparency issues, regional disparities in implementation, and algorithmic complexity contribute to the difficulties in auditor practices in adopting AI tools. These challenges drive the need to establish policy-practice configurations for the effective implementation of audit technologies based on AI.
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John Joshua C. Rañeses, Dr. Ain Bemisal Alavi, Rafia et al.· Journal of Business Insight...· 0 citations
This study aims to examine auditors' readiness and attitudinal profiles toward for implementing artificial intelligence (AI) in public sector external auditing, comparing professionals working in institutions affiliated with the European Organisation of Supreme Audit Institutions (EUROSAI) and the European Organisation of Regional Audit Institutions (EURORAI).
Using survey data from 557 external public auditors, the study combines comparative tests, k-means clustering to identify readiness profiles, and multi-group partial least squares structural equation modeling to assess whether the determinants of willingness to implement AI differ across the two institutional settings.
The results suggest an early-stage adoption pattern in which auditors perceive strong AI benefits – particularly in automation, data analysis, and text processing – yet willingness to implement AI remains moderate. This gap is consistent with an audit context where adoption requires not only expected gains but also confidence in explainability and evidential traceability, alongside sufficient organisational support. The comparison between EUROSAI and EURORAI indicates selective rather than pervasive differences: willingness is more closely linked to enabling conditions in EURORAI, while effort-related perceptions play a more visible role in EUROSAI. Additionally, three readiness profiles (sceptical, moderate and enthusiastic) highlight substantial internal heterogeneity.
The study brings public audit institutions into the public management debate on AI-enabled digital transformation by integrating auditor heterogeneity with a comparative perspective. It shows that AI-acceptance mechanisms are selectively conditioned by institutional setting, underscoring the importance of considering both individual perceptions and contextual conditions when designing AI implementation strategies in public auditing.
Natalia Alonso-Morales, Alejandro Sáez-Martín, A. López-Hernández et al.· International Journal of Pub...· 0 citations
This paper addresses the gap in the literature regarding the adoption of Big Data Analytics (BDA) by auditors and its impact on professional skepticism (PS). It explores how auditors’ personal traits, such as prior experience, self-efficacy, and trust, influence their perceived usefulness (PU) and perceived ease of use (PEU) of BDA. Additionally, it examines how these perceptions affect their behavioral intentions (BI) to adopt BDA tools, whether this leads to actual usage (AU), and subsequently investigates whether the AU of BDA impacts PS. A questionnaire was prepared and distributed to 94 external auditors from the Big Four auditing firms in Palestine (86% response rate) by adopting the census method. The findings indicate that certain auditors’ characteristics positively influence perceptions of BDA’s usefulness and ease of use, subsequently affecting adoption intentions and actual adoption. However, not all auditors’ characteristics show this positive influence. Furthermore, the AU of BDA is found to significantly impact PS, suggesting that BDA tools could enhance audit quality. The study’s results emphasize the importance of keeping PS strong as technology evolves. These findings are essential for audit firms looking to use BDA to improve audit quality.
Moath Abdelkarim Abu Al Rob, Mohd Nazli Mohd Nor, Zalailah Salleh· Contemporary Management Rese...· 0 citations