economies

Article
The Role of IT Governance in the Integration of AI in
Accounting and Auditing Operations
Faozi A. Almaqtari

Accounting and Finance Department, College of Business Administration, A’Sharqiyah University (ASU),
P.O. Box 42, Ibra 400, Oman; faozi.almaqtari@asu.edu.om

Abstract: IT governance is a framework that manages the efficient use of information technology
within an organization, focusing on strategic alignment, risk management, resource management,
performance measurement, compliance, and value delivery. This study investigates the role of IT
governance in integrating artificial intelligence (AI) in accounting and auditing operations. Data
were collected from 228 participants from Saudi Arabia using a combination of convenience sampling
and snowball sampling methods. The collected data were then analyzed using structural equation
modeling. Unexpectedly, the results demonstrate that AI, big data analytics, cloud computing, and
deep learning technologies significantly enhance accounting and auditing functions’ efficiency and
decision-making capabilities, leading to improved financial reporting and audit processes. The results
highlight that IT governance plays a crucial role in managing the complexities of AI integration,
aligning business strategies with AI-enabled technologies, and facilitating these advancements.
This research fills a gap in previous research and adds significantly to the academic literature by
improving the understanding of integrating AI into accounting and auditing processes. It builds
on existing theoretical frameworks by investigating the role of IT governance in promoting AI
adoption. The findings provide valuable insights for accounting and auditing experts, IT specialists,
and organizational leaders. The study provides practical insights on deploying AI-driven technology
in organizations to enhance auditing procedures and financial reporting. In a societal context, it
highlights the broader implications of AI on transparency, accountability, and trust in financial
reporting. Finally, the study offers practitioners, policymakers, and scholars valuable insights
on leveraging AI advancements to optimize accounting and auditing operations. It highlights IT
Citation: Almaqtari, Faozi A. 2024.
governance as an essential tool for effectively integrating AI technologies in accounting and auditing
The Role of IT Governance in the operations. However, successful implementation encounters significant organizational challenges
Integration of AI in Accounting and like organizational support, training, data sovereignty, and regulatory compliance.
Auditing Operations. Economies 12:
199. https://doi.org/10.3390/ Keywords: big data; cloud computing; deep learning; IT governance; auditing functions; accounting
economies12080199 operations
Academic Editor: Periklis Gogas

Received: 27 May 2024

Revised: 22 July 2024 1. Introduction
Accepted: 23 July 2024
Integrating emerging technologies has significantly advanced accounting operations,
Published: 1 August 2024
transforming these processes (Enholm et al. 2022; Han et al. 2023). New technology has
significantly improved accounting operations, enhancing accuracy and efficiency (Shaffer
et al. 2020; Yoon 2020). Automated routine tasks allow accountants to focus on strategic
Copyright: © 2024 by the author. tasks like data entry, invoice processing, and payroll management (Kopalle et al. 2022;
Licensee MDPI, Basel, Switzerland. Vărzaru 2022; Wamba-Taguimdje et al. 2020). Machine learning and big data (BD) analytics
This article is an open access article algorithms can process large-scale financial data quickly, enabling better risk management,
distributed under the terms and financial forecasting, and decision-making (Di Vaio et al. 2020; Salijeni et al. 2019; Schmitz
conditions of the Creative Commons and Leoni 2019; Vărzaru 2022). Similarly, cloud computing enables real-time reporting,
Attribution (CC BY) license (https:// while AI-driven audit tools improve audit quality by identifying irregularities and fraud
creativecommons.org/licenses/by/ (Faccia et al. 2019; Issa et al. 2016; Sledgianowski et al. 2017). Further, AI-powered software
4.0/).

Economies 2024, 12, 199. https://doi.org/10.3390/economies12080199 https://www.mdpi.com/journal/economies

Economies 2024, 12, 199 2 of 24

can quickly identify inconsistencies and reconcile accounts while continuous monitoring re-
duces non-compliance risks and legal issues. AI algorithms can also provide individualized
financial advice by analyzing a company’s unique data and market circumstances, allowing
companies to modify their financial plans to suit their specific needs (Cabrera-Sánchez et al.
2021; O’Leary 2009).
However, rapid technological advancements pose complex challenges for organiza-
tions in integrating AI with accounting and auditing functions. AI has the potential to
transform accounting and auditing but faces challenges such as data quality and bias, which
can lead to incorrect outcomes and poor decision-making (Brown-Liburd and Vasarhelyi
2015; Lee and Tajudeen 2020; Noor and Mansor 2019; Schmitz and Leoni 2019). Because
some current systems may not be built to manage the needs of AI data, integrating AI
with them can be difficult and expensive (Lee and Tajudeen 2020; Munoko et al. 2020;
O’Leary 2009; Yoon 2020). Additionally, the accounting and auditing occupations may be
experiencing a skills deficit in their workforce, which calls for upskilling and reskilling
to comprehend AI discoveries and identify biases (Shaffer et al. 2020; Sutton et al. 2016).
The growing interest in AI’s potential to improve accounting and auditing techniques
is mainly due to the need to understand how information technology governance (ITG)
facilitates such interactions (Abdullah and Almaqtari 2024). Ineffective governance can
hinder the potential benefits of AI technology, making it crucial for firms to optimize their
processes (Wang 2022; Xia et al. 2022). A robust IT governance structure can help close the
gap between AI adoption and transformation in accounting and auditing (Papagiannidis
et al. 2023). This structure includes data governance, which creates explicit norms and
procedures for data collection, storage, and access, ensuring data quality and reducing
bias concerns (Arnaboldi et al. 2017). IT governance also sets standards for data formats
and communication protocols, making it easier to integrate AI tools with current software
(Abdullah and Almaqtari 2024).
Various studies indicate the essential role of IT governance in ensuring these technolo-
gies’ ethical, secure, and efficient use (Bradley and Soule 2018; Erasmus and Marnewick
2021; Teixeira and Tavares-Lehmann 2022). IT governance establishes unified data gover-
nance policies to ensure data quality, security, and ethical use and defines ethical guidelines
for deploying AI algorithms to maintain transparency, fairness, and accountability (Nedbal
et al. 2011; Schmidt and Kolbe 2011; Molla et al. 2009). Effective IT governance is critical for
leveraging AI while mitigating associated risks (Wang 2022; Xia et al. 2022). It provides
a framework for informed technology investment decisions, regulatory compliance, and
aligning technology strategies with business objectives (Bradley and Soule 2018; Teixeira
and Tavares-Lehmann 2022; Yu et al. 2022).
This research explores the mediating effect of IT governance on the relationship
between AI technologies and the transformation of accounting and auditing operations
in some Saudi business organizations. Saudi Arabia is undergoing significant economic
transformation under Vision 2030, aiming to diversify the economy and modernize sectors
like finance and technology. The integration of AI in accounting and auditing presents
unique challenges and opportunities. Cultural and institutional factors, as well as regional
technological advancements, can affect the adoption and impact of AI. Studying AI’s impact
in Saudi Arabia can provide insights for other emerging economies. The findings can inform
policymakers, practitioners, and academics, benefiting international readers with similar
economic structures or development goals. Accordingly, the research questions raised in
this regard are: What is the influence of AI on accounting and auditing operations, and what
is the role of IT governance in this regard? These research questions become paramount to
understanding the implications and opportunities presented by AI integration.
Economies 2024, 12, 199 3 of 24

Accordingly, the present research aims to investigate the effect of technological ad-
vancements such as AI (big data analytics, deep learning, and cloud computing) on trans-
forming several accounting and auditing functions. Further, the research aims to explore
the role of IT governance in the relationship between AI and accounting and auditing
operations. The current research provides a unique and novel contribution to the existing
stock of knowledge and bridges an existing gap in prior studies. The research bridges an
existing gap by exploring how these technologies reshape specific auditing and accounting
practices within the country’s unique regulatory and operational landscape. Several prior
studies exist that investigate the effect of AI on auditing and accounting practices (Damerji
and Salimi 2021; Kokina and Davenport 2017; Lee and Tajudeen 2020; Munoko et al. 2020;
Shaffer et al. 2020; Sutton et al. 2016; Vărzaru 2022; Zhang et al. 2020). AI is more intelligent
than traditional information systems, which can bridge the association between traditional
accounting information systems and intelligent systems, leading to increased automation
and optimization of information systems (Damerji and Salimi 2021). AI is revolutionizing
accounting and auditing by automating repetitive tasks, providing deeper insights into
financial and non-financial performance, and improving decision-making. AI systems can
scan large datasets, detect anomalies, and increase audit quality. AI can automate data
collection, extraction, and validation, allowing auditors to focus on higher-level judgment
activities (Kokina and Davenport 2017).
However, the specific effect on auditing and accounting practices such as costing,
financial planning, taxation, audit planning, audit process, and audit reporting still needs
more exploration. Moreover, the study provides a novel contribution by exploring the me-
diating effect of IT governance on the relationship between AI and auditing and accounting
practices. Despite the fact that there are some studies that investigate IT governance in
several contexts (Cath 2018; Elazhary et al. 2022; Hardin-Ramanan et al. 2018; Kostka et al.
2020; Prakash and Ambedkar 2022; Simonsson et al. 2010), there are no studies that explore
the role of IT governance in the relationship between AI and auditing and accounting
practices, especially in the context of emerging countries.
The research contributes to the existing knowledge by enhancing IT governance knowl-
edge in the context of new technologies and accounting operations. The research aligns with
current theoretical frameworks like the Resource-Based View (RBV) (Wamba-Taguimdje
et al. 2020) and the Technology–Organization–Environment (TOE) framework (Gomez
2018), providing a theoretical framework for understanding the interactions between
developing technologies, IT governance, and accounting activities. The study employs
sophisticated research methodologies, such as structural equation modeling (SEM), to
investigate the intricate interactions between variables. It also provides research-based
perspectives from Saudi Arabia, enhancing the generalization and application of the find-
ings. The study provides practical guidance for practitioners and policymakers in Saudi
Arabia on incorporating cutting-edge technologies into accounting processes. It offers
recommendations for implementing governance mechanisms to maximize the advantages
of AI and other technologies in accounting procedures. The findings could improve the
effectiveness and capacity of accounting operations, leading to better risk management,
financial reporting, and overall organizational success. The study supports Saudi Vision
2030, which promotes technical innovation and advancement in various industries, includ-
ing accounting and finance. By promoting the adoption of emerging technology, the study
contributes to achieving Vision 2030’s goals and the nation’s economic development.
The study structure starts with an introduction outlining the research objectives.
A literature review follows this in Section 2, which establishes the theoretical foundations
and hypotheses. Section 3 details the methodology and explains the research approach.
Section 4 presents the results and analysis, while Section 5 discusses the implications of the
empirical findings. Finally, Section 6 synthesizes the key insights and suggests directions
for future research.
Economies 2024, 12, 199 4 of 24

2. Literature Review and Hypotheses Development

2.1. The Saudi Context
The Saudi Vision 2030 emphasizes the potential of artificial intelligence for digital
transformation and creative services. The government is implementing significant initia-
tives, but the extent to which Saudi enterprises are ready for this transformation needs
more research investigation, considering factors affecting readiness and potential issues
(Abdullah and Almaqtari 2024). Further, Saudi Arabia’s National Transformation Program
(NTP) aims to achieve the Saudi Vision 2030 by accelerating digital infrastructure projects in
higher education institutions. The NTP has set national development objectives to advance
AI research (Alotaibi and Alshehri 2023). Saudi Arabia has invested significantly in emerg-
ing technologies to drive economic growth and achieve the objectives outlined in Vision
2030 (Alanazi 2023; Khan et al. 2022). The Saudi Arabian government prioritizes cybersecu-
rity and other AI tools, with AI projected to contribute to the country’s GDP by 2030. Saudi
Arabia is projected to generate up to $135.2 billion in economic output by 2030, accounting
for 12.4% of the country’s GDP (Jain 2018). The AI market is expanding rapidly, with Vision
2030 aiming for the nation to rank in the top 20 of the world’s competitiveness index. By the
end of 2024, Saudi Arabia is expected to spend over $11 billion on IT, primarily cloud and
artificial intelligence technologies (The Ministry of Finance 2024). A Deloitte survey shows
that 63% of Saudi businesses are using or intending to use AI technologies to improve
business processes, such as accounting and auditing. However, cybersecurity issues remain
a concern, with over 70% of enterprises experiencing at least one hack the previous year
(Deloitte 2023). Thus, Saudi organizations increasingly recognize IT as a necessity rather
than a luxury, utilizing it to enhance performance, meet customer expectations, and reduce
costs while maintaining service quality. The country’s dynamic blend of traditional culture
and modern economic and corporate realities makes it an ideal study location (Abu-Musa
2008). Saudi Arabia is advancing AI adoption in various sectors, including education, but
its implementation of AI-based learning outcomes is still in its early stages (Alotaibi and
Alshehri 2023). Artificial intelligence techniques such as machine learning and predictive
analysis are used to predict financial trends and analyze performance (Rahman et al. 2022).
The Kingdom of Saudi Arabia is integrating big data and the Internet of Things in various
industries, including higher education, banking, and healthcare. Schools use big data to
optimize resource allocation, student enrollment, and academic success.
The accounting and auditing profession in the Kingdom has traditionally relied on
manual processes, paper documentation, and manual inspection, which increase the risk
of errors and complicate the search and retrieval process. However, adopting modern
technology has led to significant changes in the profession (Abdullah and Almaqtari 2024).
Saudi Arabia is embracing digital transformation in its accounting and auditing profession,
with companies investing in enterprise resource management systems (ERP) like SAP and
Oracle to automate processes and standardize financial data (AlGhazzawi 2020). Thus, as
Saudi Arabia is transforming into the digital era, the transformative influence of AI on
its accounting and auditing operations has become apparent. In Saudi Arabia, adopting
AI and technological information can lead to new changes in structure (Rahman et al.
2022), such as raising necessity and accuracy, improving transparency and accountability
(Almaqtari et al. 2022; Razi and Madani 2013), developing modern skills of accountants
and auditors in data technology and analysis (Rahman et al. 2022), and enhancing partial
capacity enhancement (Al-Htaybat et al. 2018). The banking industry uses big data to
monitor customer service feedback and cash flow (Rahman et al. 2022). Companies benefit
from cloud providers’ advanced cybersecurity services to protect sensitive financial data
(Alhumoudi and Juayr 2023; Al-Baity 2023). Saudi Arabia’s financial sector is transforming
significantly due to the integration of cloud computing, big data analytics, and artificial
intelligence into accounting and auditing methods (Alzahrani 2024; Hamza et al. 2024).
Economies 2024, 12, 199 5 of 24

2.2. The Impact of Artificial Intelligence on Accounting and Auditing

AI has revolutionized the accounting and auditing by enhancing accuracy, efficiency,
and strategic value (Faccia et al. 2019; Gotthardt et al. 2020; Lee and Tajudeen 2020; Shaffer
et al. 2020; Vărzaru 2022; Warren et al. 2015; Yoon 2020). AI in accounting has the potential
to advance the field by prioritizing analytical and interpretive abilities over administrative
tasks (O’Leary 2009). It can examine complete datasets (Faccia et al. 2019; Lee and Tajudeen
2020), detect anomalies (Brown-Liburd and Vasarhelyi 2015), and use predictive analytics
(Earley 2015; Yoon 2020) to identify fraud and abnormalities more quickly and precisely
(Lee and Tajudeen 2020; Yoon 2020). Further, the continuous monitoring of financial
activity and transactions allows for real-time oversight, making audits more timely and
strengthening the capacity to identify and address problems as they arise (Munoko et al.
2020). Based on this discussion, the following hypothesis is posited:

H1: Artificial intelligence tools have a significant influence on transforming accounting and
auditing operations in Saudi Arabia.

AI tools are revolutionizing accounting and auditing by handling large amounts of

financial data (Faccia et al. 2019; Gotthardt et al. 2020; Huerta and Jensen 2017; Lee and
Tajudeen 2020; Sutton et al. 2016), identifying anomalies and trends (Earley 2015; Schmitz
and Leoni 2019), and improving financial forecasting (Shaffer et al. 2020; Yoon 2020),
fraud detection (Earley 2015; Hooda et al. 2020; Noor and Mansor 2019), and decision-
making (Huerta and Jensen 2017; Kopalle et al. 2022; Sutton et al. 2016; Yoon 2020). Big
data analytics enable auditors to analyze complete datasets (Alles and Gray 2016; Hooda
et al. 2020;; Kopalle et al. 2022; Sutton et al. 2016; Yoon 2020), detecting trends (Faccia
et al. 2019; Yoon 2020), abnormalities (Cockcroft and Russell 2018; Salijeni et al. 2019;
Warren et al. 2015), and potential risks (Brown-Liburd and Vasarhelyi 2015; Gotthardt
et al. 2020), leading to more precise and thorough audit preparation and planning. In
the same context, big data solutions automate data gathering and analysis (Shaffer et al.
2020; Yoon 2020) in the audit implementation and workflow, reducing manual labor and
increasing productivity (Gotthardt et al. 2020; Wamba-Taguimdje et al. 2020). Continuous
auditing, where transactions are tracked and examined as they occur, facilitates the early
identification of problems and timely resolution (Abdullah and Almaqtari 2024). Further,
the thorough analysis of big data improves audit reports, providing a comprehensive, data-
driven picture of an organization’s financial health (Alles and Gray 2016; Brown-Liburd
and Vasarhelyi 2015; Earley 2015; Warren et al. 2015). Big data analytics in accounting also
increases costing accuracy by examining vast amounts of operational and transactional
data, allowing accountants to pinpoint cost centers and distribute expenses more accurately
(Cockcroft and Russell 2018; Earley 2015; Huerta and Jensen 2017). It also integrates real-
time data from multiple sources to ensure compliance and accuracy for financial reporting
and taxation, reducing the possibility of mistakes and fines (Faccia et al. 2019; Lee and
Tajudeen 2020; Munoko et al. 2020; Shaffer et al. 2020; Vărzaru 2022). In addition, big data
impacts budgeting and strategic planning, enabling organizations to create more precise
and flexible budgets by examining past data and forecast patterns (Abdullah and Almaqtari
2024). Thus, the following hypothesis is formulated:

H1a: Big data has a significant influence on transforming accounting and auditing operations in
Saudi Arabia.

Machine learning algorithms and robotic process automation (RPA) are examples of AI-
powered systems that streamline tasks like data entry, invoice processing, and reconciliation
(Gotthardt et al. 2020; Lee and Tajudeen 2020; Vărzaru 2022). AI also enables sophisticated
data analytics, providing accountants with a better understanding of financial performance
and patterns, enabling better strategic planning and decision-making (Faccia et al. 2019; Lee
and Tajudeen 2020; Yoon 2020). Deep learning algorithms, which continuously learn from
Economies 2024, 12, 199 6 of 24

large datasets, automate the detection of fraud and abnormalities in auditing, improving
audit preparation and planning by pointing out high-risk locations that need further
investigation (Brown-Liburd and Vasarhelyi 2015; Gotthardt et al. 2020; Munoko et al. 2020;
Yoon 2020). Thus, the following hypothesis is formulated:

H1b: Deep learning has a significant influence on transforming accounting and auditing operations
in Saudi Arabia.

Accounting and auditing have transformed because cloud computing has increased
efficacy, accuracy, and efficiency (Abdullah and Almaqtari 2024; Chen 2021). It allows
accountants and auditors to access financial data (Groomer and Murthy 2018; Lee and
Tajudeen 2020) anywhere (Faccia et al. 2019), reducing human error and allowing for more
efficient reporting and audits (Faccia et al. 2019; Lee and Tajudeen 2020). Cloud-based
accounting software like Xero, Sage, SAP, Automation Anywhere, and Financio automate
repetitive operations, reducing human error risk (Lee and Tajudeen 2020). Cloud computing
also offers financial savings by eliminating the need for expensive physical IT infrastructure
and allowing businesses to optimize operating costs through pay-as-you-go cloud services
(Di Vaio et al. 2020; Salijeni et al. 2019; Wamba-Taguimdje et al. 2020).
Cloud service providers invest in security measures to protect against hacking (Got-
thardt et al. 2020) and data breaches (Munoko et al. 2020), offering advanced features
like encryption (Gomez 2018; Yoon 2020), multi-factor authentication (Schmitz and Leoni
2019; Yoon 2020), and frequent security assessments (Brown-Liburd and Vasarhelyi 2015;
Gotthardt et al. 2020). Cloud computing solutions also provide numerous disaster recovery
options (Faccia et al. 2019), ensuring business continuity and allowing quick recovery from
setbacks (Almaqtari et al. 2022). Moreover, advanced analytics and real-time reporting
enable accountants to create comprehensive financial reports, conduct trend analysis, and
make data-driven decisions (Sutton et al. 2016; Yoon 2020). Real-time data access also
enables auditors to conduct continuous audits, making audits more efficient and timely
(Brown-Liburd and Vasarhelyi 2015; Shaffer et al. 2020; Yoon 2020). Because cloud comput-
ing gives users access to advanced analytical tools and real-time financial data, it makes
budgeting and strategic planning easier (Abdullah and Almaqtari 2024). To this end, the
following hypothesis is developed:

H1c: Cloud computing has a significant influence on transforming accounting and auditing
operations in Saudi Arabia.

2.3. The Mediating Role of IT Governance on the Relationship between Artificial Intelligence and
Accounting and Auditing
While AI has revolutionized accounting and auditing, effective information technology
governance (ITG) is essential for effectively using these tools. There are challenges to
integrating AI in accounting and auditing, such as alignment and strategic focus (Abdullah
and Almaqtari 2024). Moreover, it is crucial to ensure that AI systems are impartial,
transparent, and safe, as any biases or faults in the algorithms could lead to severe mistakes
or unethical behavior (Faccia et al. 2019; Gotthardt et al. 2020; Sutton et al. 2016; Yoon 2020).
Therefore, the successful application of AI technologies in accounting and auditing depends
on robust IT governance (Abdullah and Almaqtari 2024), which ensures the ethical, secure,
and successful deployment and use of AI tools (Gotthardt et al. 2020; Papagiannidis et al.
2023). Further, there are issues to consider, such as bias (Gotthardt et al. 2020; Sutton et al.
2016), data quality (Sutton et al. 2016; Yoon 2020), lack of interpretability and transparency
(Faccia et al. 2019; Gotthardt et al. 2020; Yoon 2020), interaction with current systems (Al-
Hattami 2023; Al-Hattami and Almaqtari 2023; Sutton et al. 2016), and skills deficiencies in
the workforce (Lee and Tajudeen 2020; Munoko et al. 2020). Thus, to address these issues,
risk management, standardization and accountability, data security and privacy rules, and
IT governance are essential (Abdullah and Almaqtari 2024).
Economies 2024, 12, 199 7 of 24

Integrating AI tools, such as “Big Data, Deep Learning, and Cloud Computing”,
revolutionizes accounting and auditing operations by improving productivity, accuracy,
and strategic insight. However, substantial information technology governance (ITG) is
essential for effectively implementing these tools. ITG ensures that AI technologies are
implemented and used efficiently, safely, and morally responsibly (Awwad and El Khoury
2021; Cath 2018). ITG frameworks guarantee data integrity and confidentiality by ensuring
AI applications follow legal and security requirements (Awwad and El Khoury 2021; Floridi
2018; Savtschenko et al. 2017; Winfield et al. 2019). They also require documentation and
frequent audits of AI systems to preserve the integrity of audit and accounting procedures
(Abdullah and Almaqtari 2024; Papagiannidis et al. 2023). Consequently, the following
hypothesis is stated:

H2: IT Governance mediates the relationship between AI tools (“Big Data, Deep Learning, and
Cloud Computing”) and digitized accounting and auditing operations in Saudi Arabia.

3. Research Design and Method

3.1. Sample and Tools of Analysis
The data used for the current study was collected from a research population of
participants from different Saudi Arabian business organizations. The data was collected
using several non-probability sampling methods, including convenience sampling. Further,
the snowball sampling technique was also used to enhance the targeted sample and obtain
more relevant data from the targeted respondents. At the initial stage, the study used
convenience sampling to gather many responses quickly and affordably, eliminating the
need for random sampling (Gomez 2018; Al Omari 2016; Wilkin et al. 2016). It is beneficial
when limited time and resources allow early findings and pattern identification (Almaqtari
et al. 2022; Wilkin et al. 2016). However, it has some limitations, including selection bias
and limited generalizability. The study collected data from multiple sources and digital
platforms via Google Docs and social media platforms to address this issue.
Further, we used another sampling technique, which is snowball sampling. The study
used snowball sampling to overcome the bias issues that may arise from convenience
sampling and to obtain more relevant and reliable data from specialized and respective
respondents. In this method, the initial respondents find new participants from their
network, increasing the sample size and possibly reaching people who might have yet
to be reached by conventional sampling techniques (Gomez 2018; Al Omari 2016). This
combination strategy improves the diversity and representativeness of the sample and is
consistent with previous literature approaches (Abdullah and Almaqtari 2024; Al-Hattami
and Almaqtari 2023; Almaqtari et al. 2022).
Several respondents from several Saudi Arabian organizations were targeted to col-
lect the data required for the study. The sample includes respondents from accounting
professionals, external and internal auditors, firms’ board members, chief and financial
executive officers, managers, and other IT specialists. We surveyed (i) their perception of
the impact of AI on accounting and auditing operations and (ii) the impact of information
technological governance in this context. These respondents were targeted as they deal
with accounting operations and AI tools, adopt accounting and management systems, audit
these systems, and operate and plan them. Thus, their perception could help investigate
real-world practices of integrating AI tools into accounting transactions. Accordingly, the
study aimed to collect sufficient responses to analyze the data. For this purpose, the study
used G-Power software tools and other sampling techniques to estimate the data required
for the current study.
Economies 2024, 12, 199 8 of 24

3.2. Questionnaire Design and the Research Instrument

This study investigates the relationship between emerging technologies, IT gover-
nance, accounting, and auditing operations. It uses a research instrument incorporating
AI tools, including “Big Data, Deep Learning, Cloud Computing”, IT governance, and
accounting and auditing operations constructs. The survey used in the current research
was designed following similar prior studies that highlight the impact of AI and cloud
computing on organizational processes (Di Vaio et al. 2020; Losbichler and Lehner 2021;
Papagiannidis et al. 2023). The study also followed other prior studies that emphasize the
importance of IT governance in enabling enterprises to effectively utilize these technologies
(Abdullah and Almaqtari 2024; Almaqtari et al. 2022). The research instrument commences
with the demographic variables section that captures the respondents’ characteristics. Then,
the survey provides insights into audit planning, procedure, and reporting and highlights
the role of IT governance in integrating AI, big data, and cloud computing in accounting
and auditing functions. The questionnaire captures the complexities of emerging technolo-
gies and their impact on accounting operations and auditing. The survey was designed
based on nine items that capture AI variables (three items each), four items that measure
IT governance, nine items that proxy auditing practices, and nine items for accounting
practices. Table 1 presents the measurements and synthesized variables corresponding to
each construct.

Table 1. Measurements of the variables.

Constructs Indicators Symbol Items Synthesized Literature

“Big Data” BD 3 (Mikalef and Gupta 2021)
AI “Deep Learning” DL 3 (Sun and Vasarhelyi 2018; Issa et al. 2016)
“Cloud Computing” CC 3 (Mikalef and Gupta 2021)
IT Governance IT Governance ITG 4 (Almaqtari et al. 2024)
Audit Preparation and Planning ADPL 4
Auditing functions Audit Process ADP 3 (Issa et al. 2016)
Audit Reporting ADRP 2
Costing COS 3
Accounting operations Reporting and Taxation RT 3 (Aqlan 2021)
Strategic Planning and Budgeting STB 3

3.3. Statistical Setting

This study employed a comprehensive statistical setting to ensure the robustness and
validity of the results. The study follows Li et al. (2022) in some aspects of the statistical
tools used. At the initial stage, filter analysis was conducted, including frequency analysis,
normality, and extreme value analysis. The process included data filtering and visualization
to remove incomplete or inconsistent responses, frequency, and descriptive statistics to
understand the distribution of responses across different categories, as well as normality
analysis using kurtosis and skewness to assess normality. Further, the study estimates
the data using a sampling adequacy test using SPSS 23, which reports that the sample is
adequate. Sampling adequacy, KMO, and Bartlett’s Tests were used to evaluate a sample
of 228 surveys, and the results verified that the sample size was adequate for factor and
predictive analysis. According to the findings, the data were appropriate for exploratory
factor analysis. The sampling adequacy was assessed using the Kaiser–Meyer–Olkin (KMO)
Test, with a KMO value of 0.927, which is higher than the criterion value (0.50) (Krichene
and Baklouti 2020), indicating an adequate sample size for factor analysis. Confirmatory
factor analysis (CFA) was used to test the validity of the constructs and confirm whether the
data fit the hypothesized measurement model. Reliability and validity analyses were also
conducted using Cronbach’s alpha (CA) to assess the internal consistency of the constructs.
Rho_A assesses internal consistency dependability, suggesting a group of items in a scale
or construct that measures the same principle (Al-Hattami 2023). The reliability of a latent
Economies 2024, 12, 199 9 of 24

construct was evaluated using composite reliability, which takes into account both the
construct’s variation and the error variance. Rho_A values vary between 0 and 1, with
larger values suggesting stronger internal consistency among the components (Al-Hattami
and Almaqtari 2023).
CR values vary from 0 to 1, with values greater than 0.7 being accepted as reliable
(Al-Hattami 2023). AVE assesses convergent validity by comparing the variance captured
by latent concept indicators to the measurement error (Almaqtari et al. 2022). Convergent
validity refers to the degree to which various indicators of a latent construct are connected,
implying that they measure the same underlying construct. The AVE values range from
0 to 1, with higher values suggesting that the construct’s indicators account for a more
significant proportion of the variance. An average variance extracted (AVE) value greater
than 0.4 should be considered evidence of convergent validity (Hu and Bentler 1998; Hu
et al. 2022). The results show that constructs with AVE values greater than 0.5 indicate good
convergent validity. Finally, partial least squares (PLS) modeling was used to estimate the
results. The interrelationships among the constructs were comprehensively analyzed using
path analysis, confirmatory factor analysis (CFA), and structural equation modeling (SEM)
to explore the effects and relationships among the variables. Additionally, all hypotheses
(H1, H1a, H1b, H1c, H1d, and H2) were tested using structural equation modeling (SEM)
to test the hypothesized relationships among the constructs.

4. Empirical Results
4.1. Model’s Measurement
Confirmatory Factor Analysis and Reliability Analysis
The confirmatory factor analysis (CFA) and reliability analysis of the measurement
model reveal robust psychometric properties across multiple constructs. The Cronbach’s
alpha (CA), rho_A, composite reliability (CR), and average variance extracted (AVE) values
are reported for each construct, demonstrating strong conceptual validity and internal
consistency. Table 2 demonstrates their findings. The big data (BD) construct has high item
loadings, with Cronbach’s alpha values above acceptable thresholds. Deep learning (DL)
also shows high item loadings, indicating its reliability and reliability. Cloud computing
(CC) has item loadings that exceed minimum acceptable levels, indicating acceptable
reliability and validity. IT governance (ITG) items load strongly, indicating excellent
reliability and validity. Audit preparation and planning (ADPL) items have high loadings,
which confirms their reliability. Audit process (ADP) items have loadings that indicate
good internal consistency and validity.
Audit findings report (ADRP) items have high factor loadings, with CA values exceed-
ing 0.70, CR values above 0.80, and AVE values well above 0.50. Strategic planning and
budgeting (STP) items load strongly, reflecting excellent reliability and validity. Reporting
and taxation (RT) items show high loadings, confirming strong reliability and validity. Cost-
ing (COS) items have high factor loadings, indicating outstanding reliability and validity.
Overall, the constructs demonstrate high reliability and validity, with CA values exceeding
0.70, CR values above 0.80, and AVE values well above 0.50, aligning with recommended
threshold values for confirming the adequacy of measurement models. The highest CA
value is observed in the Costing (COS) construct, while the lowest is still acceptable in
cloud computing (CC). This indicates that the measurement model is robust and suitable
for further structural analysis.
Figure 1 shows the confirmatory factor analysis (CFA) model. The CFA model is typi-
cally estimated using SmartPLS software, which examines the interrelationships between
latent variables and their observed indicators. It comprises the latent variables, observable
indicators, directional relationships, and factor loadings. The model examines the relation-
ships between AI variables, including big data, deep learning, and cloud computing, which
are independent variables. IT governance is a mediating variable between these variables
and auditing and accounting practices (DVs).
Economies 2024, 12, 199 10 of 24

Table 2. Factor loadings and CFA results.

Items 1 2 3 4 5 6 7 8 9 10 CA rho_A CR AVE

BD1 0.878
BD2 0.905 0.844 0.849 0.906 0.763
BD3 0.837
DL1 0.900
DL2 0.858 0.866 0.874 0.918 0.789
DL3 0.906
CC1 0.801
CC2 0.832 0.751 0.752 0.857 0.667
CC3 0.816
ITG1 0.844
ITG2 0.905
0.884 0.888 0.920 0.743
ITG3 0.835
ITG4 0.861
ADPL1 0.775
ADPL2 0.906 0.812 0.823 0.889 0.729
ADPL3 0.875
ADP1 0.858
ADP2 0.851
0.858 0.875 0.903 0.699
ADP3 0.789
ADP4 0.843
ADRP1 0.900
0.770 0.771 0.897 0.813
ADRP2 0.904
STB1 0.909
STB2 0.913 0.882 0.883 0.927 0.810
STB3 0.877
RT1 0.843
RT2 0.924 0.877 0.877 0.925 0.804
RT3 0.920
COS1 0.932
COS2 0.923 0.919 0.919 0.948 0.860
COS3 0.927
“(1) Big Data, (2) Cloud _Computing, (3) Deep _Learning, (4) IT Governance, (5) Audit preparation and planning,
(6) Audit implementation and workflow, (7) Audit Findings Report, (8) Strategic Planning and Budgeting,
(9) Reporting &_Taxation, (10) Costing”.

4.2. Direct Effect

Figure 2 illustrates the hypothesized structural model for the variables under inves-
tigation. Figure 2 provides the structural equation modeling (SEM) model that estimates
the direct and indirect relationship between the independent variables, AI indicators, and
the dependent variables represented by accounting and auditing operations. The mediat-
ing effect of IT governance on the relationship between the independent and dependent
variables is also demonstrated in SEM.
Economies 2024, 12, x FOR PEER REVIEW 11 of 25
Economies 2024, 12, 199 11 of 24

Figure1.
Figure 1. Confirmatory
Confirmatory factor
factor analysis.
analysis.

4.2. Direct Effectin Table 3 demonstrate that AI has a positive and significant impact on infor-
The results
mation technology
Figure governance
2 illustrates (β = 1.002, p value
the hypothesized = 0.000model
structural < 0.01).for
This
thefinding suggests
variables underthat the
inves-
inclusion
tigation. of AI greatly
Figure increases
2 provides thethe efficacy of
structural IT governance.
equation modeling Additionally,
(SEM) model IT governance has
that estimates
athe
significant
direct and andindirect
positiverelationship
impact on accounting
between the 0.430, p valuevariables,
(β =independent = 0.000 < 0.01) and auditing
AI indicators, and
(β = 0.485, p value = 0.000 < 0.01) activities. These findings
the dependent variables represented by accounting and auditing operations. The mediat-imply that IT governance
significantly
ing effect of facilitates
IT governancethe execution of accounting
on the relationship and auditing
between operations.
the independent and dependent
Unexpectedly, these results
variables is also demonstrated in SEM. imply that AI significantly affects Saudi Arabia’s account-
ing and Theauditing
results in activities. This is consistent
Table 3 demonstrate with
that AI hasseveral research
a positive studies (Earley
and significant impact2015;
on
Gotthardt et al. 2020; Issa et al. 2016; Munoko et al. 2020; Schmitz and
information technology governance (β = 1.002, p value = 0.000 < 0.01). This finding suggests Leoni 2019; Sutton
et al. the
that 2016) that show
inclusion how
of AI AI has
greatly a significant
increases impact of
the efficacy onITaccounting
governance. andAdditionally,
auditing tasks.
IT
Further,
governance has a significant and positive impact on accounting (β = 0.430, p effectiveness
several studies indicate the critical role that AI plays in improving the value = 0.000
and efficiency
< 0.01) of accounting
and auditing (β = 0.485, andp auditing operations
value = 0.000 < 0.01) (Brown-Liburd
activities. Theseand Vasarhelyi
findings imply2015;
that
Earley
IT governance significantly facilitates the execution of accounting and auditing imple-
2015; Kopalle et al. 2022; Vărzaru 2022; S. Zhang et al. 2021). The successful opera-
mentation of these technologies required significant cultural and organizational changes,
tions.
and businesses faced challenges like data sovereignty and regulatory compliance. Deep
Unexpectedly, these results imply that AI significantly affects Saudi Arabia’s ac-
learning systems’ improved fraud detection capabilities and the significant workforce
counting and auditing activities. This is consistent with several research studies (Earley
upskilling investment underscored the broader implications.
2015; Gotthardt et al. 2020; Issa et al. 2016; Munoko et al. 2020; Schmitz and Leoni 2019;
Sutton et al. 2016) that show how AI has a significant impact on accounting and auditing
tasks. Further, several studies indicate the critical role that AI plays in improving the ef-
fectiveness and efficiency of accounting and auditing operations (Brown-Liburd and
Economies 2024, 12, x FOR PEER REVIEW 14 of 25
Economies 2024, 12, 199 12 of 24

Figure 2. Structural equation model.

Figure 2. Structural equation model.

4.3. Indirect Moderating Effect

Table 3. SEM estimation.
Table 4 illustrates the mediating role of ITG. Panel (A) shows that ITG significantly
Path and positively mediates
β the relationship between big data
Standard Deviation and accounting pand
T Statistics auditing
Values
BD—AI functions. The positive
0.673 coefficients indicate
0.053 that IT governance
12.704 mediates by optimizing
0.000
technological infrastructure, ensuring data quality, and providing secure and efficient au-
CC—AI 0.329 0.035 9.481 0.000
diting processes. Organizations that invest in robust IT governance structures can fully
DL—AI 0.391 of big data in their
leverage the potential 0.028 accounting and auditing
14.077 functions, 0.000
resulting in
AI—ITG more accurate decision-making,
1.002 streamlined
0.018 processes, and improved financial
56.443 outcomes.
0.000
ITG—Accounting Operations Furthermore, strong
0.430 IT governance leads
0.076to more informed5.640
decision-making, efficient
0.000 pro-
cesses, and reliable reporting.
ITG—Auditing functions 0.485 0.086 5.670 0.000
Auditing Functions—ADPL 1.017 0.023 45.003 0.000
Auditing Functions—ADP 1.052 0.016 66.225 0.000
Auditing Functions—ADRP 0.937 0.038 24.895 0.000
Accounting Operations—STB 0.999 0.012 84.235 0.000
Accounting Operations—RT 1.014 0.014 73.817 0.000
Accounting Operations—COS 0.987 0.011 87.650 0.000
Economies 2024, 12, 199 13 of 24

The path from AI to IT governance (ITG) has a β value of 1.002 with a T statistic of
56.443 and a p value of 0.000, indicating a highly significant impact. Furthermore, the
paths from ITG to accounting operations and auditing functions show significant effects
(β = 0.430 and 0.485, respectively, with p values of 0.000). These results are consistent with
the hypothesis that AI tools significantly transform accounting and auditing operations,
supporting H1. This indicates that H1, which states AI tools have a significant influence on
transforming accounting and auditing operations in Saudi Arabia, is accepted, answering
the research question of AI’s influence on accounting and auditing operations. The results
demonstrate that AI positively and significantly affects accounting and auditing practices.
This is in line with Faccia et al. (2019) and Lee and Tajudeen (2020), who found that AI
enhances the accuracy and efficiency of accounting and auditing by automating routine
tasks and providing advanced data analysis capabilities. Similarly, Brown-Liburd and
Vasarhelyi (2015) and Yoon (2020) noted that AI’s ability to process large datasets and
identify anomalies improves fraud detection and decision-making. Munoko et al. (2020)
also emphasized that continuous monitoring enabled by AI allows real-time oversight,
thus improving the timeliness and reliability of audits.
The influence of big data (BD) on AI is 0.673, indicating a T statistic of 12.704 and
a p value of 0.000. This significant positive relationship indicates that big data plays a
crucial role in enhancing AI capabilities, which, in turn, transform accounting and auditing
operations. The robust T statistic supports the hypothesis H1a. This is consistent with Alles
and Gray (2016) and Brown-Liburd and Vasarhelyi (2015), who highlighted that big data
analytics allow auditors to analyze complete datasets, detect trends, and identify risks more
effectively. Cockcroft and Russell (2018) and Shaffer et al. (2020) also noted that big data
supports continuous auditing, enabling real-time problem identification and resolution.
These findings align with the notion that big data leads to more precise audit planning and
execution, reducing manual labor and increasing productivity (Gotthardt et al. 2020).
The results also provide surprising insights. Deep learning (DL) to AI is also highly
significant, with a β value of 0.391, a T statistic of 14.077, and a p value of 0.000. These results
suggest that deep learning contributes significantly to AI’s effectiveness in transforming
accounting and auditing processes, supporting H1b. This is in line with Gotthardt et al.
(2020), who found that deep learning algorithms automate fraud detection and anomalies,
improving the accuracy of audit preparations. Munoko et al. (2020) and Yoon (2020)
also noted that by continuously learning from large datasets, these algorithms enhance
the identification of high-risk areas, facilitating more targeted and efficient audits. The
successful deployment of these technologies necessitates significant cultural adaptation
and organizational change, forcing businesses to reconsider their established processes.
Deep learning technology has greatly surpassed traditional methods for detecting financial
fraud, minimizing inconsistencies, and increasing trust in financial reports. Integrating new
technologies necessitates significant investments in workforce upskilling and reskilling,
emphasizing the importance of specialized educational efforts.
Similarly, the path from cloud computing (CC) to AI shows a significant effect, with
a β value of 0.329, a T statistic of 9.481, and a p value of 0.000. This indicates that cloud
computing significantly enhances AI functionalities, which is critical for transforming
accounting and auditing operations, thus supporting H1c. This is consistent with the
findings of Groomer and Murthy (2018) and Lee and Tajudeen (2020), who stated that cloud
computing increases the efficiency and accuracy of accounting and auditing by providing
access to financial data anytime, anywhere. Di Vaio et al. (2020) and Faccia et al. (2019) also
highlighted that cloud-based solutions automate repetitive tasks, reduce human error, and
offer significant cost savings by eliminating the need for expensive IT infrastructure. These
points align with the view that enhanced security measures and disaster recovery options
ensure business continuity and data protection (Gotthardt et al. 2020; Munoko et al. 2020).
Overall, the results provide robust evidence supporting the hypotheses. AI tools,
driven by big data, deep learning, and cloud computing, positively and significantly trans-
form accounting and auditing operations. This is consistent with the literature, which
Economies 2024, 12, 199 14 of 24

underscores the transformative potential of AI and its foundational technologies in finan-

cial operations.

4.3. Indirect Moderating Effect

Table 4 illustrates the mediating role of ITG. Panel (A) shows that ITG significantly
and positively mediates the relationship between big data and accounting and auditing
functions. The positive coefficients indicate that IT governance mediates by optimizing
technological infrastructure, ensuring data quality, and providing secure and efficient
auditing processes. Organizations that invest in robust IT governance structures can fully
leverage the potential of big data in their accounting and auditing functions, resulting in
more accurate decision-making, streamlined processes, and improved financial outcomes.
Furthermore, strong IT governance leads to more informed decision-making, efficient
processes, and reliable reporting.

Table 4. SEM estimation—indirect moderating effect.

Path β Standard Deviation T Statistics p Values

Panel (A): ITG—Big Data—Accounting and Auditing functions
Big Data—AI—ITG 0.675 0.060 11.291 0.000
Big Data—AI—ITG—Auditing Functions 0.327 0.053 6.160 0.000
Big Data—AI—ITG—Auditing Functions—Audit Preparation
0.333 0.056 5.931 0.000
and Planning
Big Data—AI—ITG—Auditing Functions—Audit
0.344 0.056 6.169 0.000
Implementation and Workflow
Big Data—AI—ITG—Auditing Functions—Audit Findings
0.306 0.053 5.803 0.000
Report
Big Data—AI—ITG—Accounting Operations 0.290 0.047 6.182 0.000
Big Data—AI—ITG—Accounting Operations—Strategic
0.290 0.048 6.076 0.000
Planning and Budgeting
Big Data—AI—ITG—Accounting Operations—Reporting and
0.294 0.049 6.051 0.000
Taxation
Big Data—AI—ITG—Accounting Operations—Costing 0.287 0.047 6.087 0.000
Panel (B): ITG—DL—Accounting and Auditing Functions
Deep_Learning—AI—ITG 0.392 0.030 13.208 0.000
Deep_Learning—AI—ITG—Auditing Functions 0.190 0.032 5.940 0.000
Deep_Learning—AI—ITG—Auditing Functions—Audit
0.193 0.034 5.764 0.000
Preparation and Planning
Deep_Learning—AI—ITG—Auditing Functions—Audit
0.200 0.033 5.988 0.000
Implementation and Workflow
Deep_Learning—AI—ITG—Auditing Functions—Audit
0.178 0.032 5.649 0.000
Findings Report
Deep_Learning—AI—ITG—Accounting Operations 0.169 0.028 6.048 0.000
Deep_Learning—AI—ITG—Accounting Operations—Strategic
0.168 0.028 5.914 0.000
Planning and Budgeting
Deep_Learning—AI—ITG—Accounting
0.171 0.029 5.904 0.000
Operations—Reporting &_Taxation
Deep_Learning—AI—ITG—Accounting Operations—Costing 0.166 0.028 5.962 0.000
Economies 2024, 12, 199 15 of 24

Table 4. Cont.

Path β Standard Deviation T Statistics p Values

Panel (C): ITG < CC—Accounting and Auditing Functions
Cloud_Computing—AI—ITG 0.330 0.035 9.528 0.000
Cloud_Computing—AI—ITG—Auditing Functions 0.160 0.037 4.335 0.000
Cloud_Computing—AI—ITG—Auditing Functions—Audit
0.162 0.038 4.295 0.000
Preparation and Planning
Cloud_Computing—AI—ITG—Auditing Functions—Audit
0.168 0.038 4.402 0.000
Implementation and Workflow
Cloud_Computing—AI—ITG—Auditing Functions—Audit
0.150 0.036 4.134 0.000
Findings Report
Cloud_Computing—AI—ITG—Accounting Operations 0.142 0.035 4.109 0.000
Cloud_Computing—AI—ITG—Accounting
0.142 0.035 4.047 0.000
Operations—Strategic Planning and Budgeting
Cloud_Computing—AI—ITG—Accounting
0.144 0.035 4.058 0.000
Operations—Reporting &_Taxation
Cloud_Computing—AI—ITG—Accounting
0.140 0.034 4.064 0.000
Operations—Costing
Panel (D): ITG—AI—Accounting and Auditing Functions
AI—ITG—Auditing Functions 0.486 0.085 5.708 0.000
AI—ITG—Auditing Functions—Audit Preparation and
0.494 0.089 5.572 0.000
Planning
AI—ITG—Auditing Functions—Audit Implementation and
0.511 0.088 5.787 0.000
Workflow
AI—ITG—Auditing Functions—Audit Findings Report 0.455 0.085 5.367 0.000
AI—ITG—Accounting Operations 0.431 0.077 5.599 0.000
AI—ITG—Accounting Operations—Strategic Planning and
0.431 0.079 5.479 0.000
Budgeting
AI—ITG—Accounting Operations—Reporting and Taxation 0.437 0.080 5.484 0.000
AI—ITG—Accounting Operations—Costing 0.426 0.077 5.513 0.000
Panel (E): ITG—Accounting and Auditing Functions
ITG—Auditing Functions—Audit Findings Report 0.454 0.085 5.339 0.000
ITG—Auditing Functions—Audit Implementation and
0.510 0.089 5.750 0.000
Workflow
ITG—Auditing Functions—Audit Preparation and Planning 0.493 0.089 5.551 0.000
ITG—Accounting Operations—Strategic Planning and
0.430 0.078 5.525 0.000
Budgeting
ITG—Accounting Operations—Reporting and Taxation 0.436 0.079 5.532 0.000
ITG—Accounting Operations—Costing 0.425 0.076 5.561 0.000

Panels (B–D) demonstrate that IT governance plays a crucial mediating role in the
relationships between deep learning, cloud computing, and AI with accounting and audit-
ing functions. IT governance establishes unified data governance policies to ensure data
consistency and ethical use. It also defines ethical guidelines for deploying AI algorithms,
ensuring transparency, fairness, and accountability (Nedbal et al. 2011; Schmidt and Kolbe
2011; Molla et al. 2009; Dhamija and Bag 2020; Handoko and Liusman 2021).
The path analysis shows significant effects from AI to IT Governance (ITG) (β = 1.002,
T = 56.443, p = 0.000) and from ITG to both accounting operations (β = 0.430, p = 0.000)
Economies 2024, 12, 199 16 of 24

and auditing functions (β = 0.485, p = 0.000). This is consistent with the hypothesis that AI
tools significantly transform accounting and auditing operations, supporting H1. Similarly,
the path coefficients indicate a significant positive relationship between big data and AI
(β = 0.673, T = 12.704, p = 0.000) and through AI, ITG, auditing functions, and accounting
operations. This strongly supports H1a. Further, the significant effect of deep learning on
AI (β = 0.391, T = 14.077, p = 0.000) and the subsequent positive impact on ITG, auditing
functions, and accounting operations support H1b. Similarly, the significant path from
cloud computing to AI (β = 0.329, T = 9.481, p = 0.000) and the subsequent positive impacts
on ITG, auditing functions, and accounting operations confirm H1c. Finally, big data,
deep learning, and cloud computing affect AI, and ITG demonstrates significant media-
tion effects, supporting H2. This signifies H2, which states that IT governance mediates
the relationship between AI tools (“Big Data, Deep Learning, and Cloud Computing”)
and digitized accounting and auditing operations in Saudi Arabia. This answers the sec-
ond research question about the role of IT governance in the relationship between AI
tools and digitized accounting and auditing operations in Saudi Arabia. This indicates
that IT governance is significant in the relationship between AI and digitized accounting
and auditing operations. The path from big data to AI to ITG (β = 0.675, T = 11.291,
p = 0.000) and from deep learning to AI to ITG (β = 0.392, T = 13.208, p = 0.000) both indicate
strong mediation by ITG. This aligns with findings from Nedbal et al. (2011), Schmidt and
Kolbe (2011), and Molla et al. (2009), indicating that effective IT governance is crucial for
organizations to leverage AI while minimizing risks (Wang 2022; Xia et al. 2022).
The results are consistent with the argument that IT governance enforces data gov-
ernance frameworks to maintain data quality and accuracy, enhancing the reliability of
financial insights. Additionally, it ensures compliance with regulatory standards in de-
ploying AI and cloud computing, adhering to legal requirements (Earley 2015; Gotthardt
et al. 2020; Sutton et al. 2016; Yoon 2020). IT governance also guides the responsible use
of resources, optimizing efficiency and cost-effectiveness in strategic planning, costing,
budgeting, taxation, and auditing (Cockcroft and Russell 2018; Earley 2015; Gotthardt
et al. 2020; Issa et al. 2016; Munoko et al. 2020; Schmitz and Leoni 2019). It includes risk
management strategies to identify, assess, and mitigate AI and cloud computing risks. The
mediating role of IT governance ensures a cohesive and responsible integration of AI and
cloud computing into accounting and auditing functions, contributing to more robust,
accurate, and responsible financial and audit practices.

5. Discussion and Implications

The study reveals robust evidence supporting the hypotheses that AI tools, driven
by big data, deep learning, and cloud computing, positively and significantly impact
transforming accounting and auditing operations in Saudi Arabia. Big data enhances AI
capabilities and transforms accounting and auditing operations. Deep learning contributes
substantially to AI’s effectiveness in transforming accounting and auditing processes (Lee
and Tajudeen 2020; Wamba-Taguimdje et al. 2020; Yoon 2020). Deep learning algorithms au-
tomate the detection of fraud and anomalies, improving the accuracy of audit preparations
(Chen 2021; Lee and Tajudeen 2020; Yoon 2020). Cloud computing significantly enhances
AI functionalities, which are critical for transforming accounting and auditing operations
(Chen 2021; Faccia et al. 2019). Cloud-based solutions automate repetitive tasks (Munoko
et al. 2020; Vărzaru 2022; Wamba-Taguimdje et al. 2020), reduce human error (Schmitz and
Leoni 2019), and offer significant cost savings by eliminating the need for expensive IT
infrastructure (Lee and Tajudeen 2020; Vărzaru 2022).
The results also indicate that the relationship between AI tools, including big data, deep
learning, cloud computing, and accounting and auditing functions, is significantly mediated
by IT governance. This is achieved by optimizing technological infrastructure, ensuring
data quality, and providing secure and efficient auditing processes. Organizations that
invest in robust IT governance structures can fully leverage the potential of big data in their
accounting and auditing functions, resulting in more accurate decision-making, streamlined
Economies 2024, 12, 199 17 of 24

processes, and improved financial outcomes (Almaqtari et al. 2022; Elazhary et al. 2022;
Papagiannidis et al. 2023; Rubino and Vitolla 2014; Turel et al. 2017). While Elazhary
et al. (2022) indicate that an organization’s decision-making and streamlined processes are
significantly influenced by IT governance, Turel et al. (2017) reveal a relationship between
board-level information technology governance and organizational performance. Similarly,
Papagiannidis et al. (2023) report that AI governance is essential for decision-making and
overcoming barriers. In this context, Rubino and Vitolla (2014) conclude that IT governance
is essential to enterprise risk management and financial outcomes. Further, IT governance
plays a crucial mediating role in the relationships between deep learning, cloud computing,
and AI with accounting information systems (Joshi et al. 2018; Papagiannidis et al. 2023;
Rubino and Vitolla 2014; Abdullah and Almaqtari 2024; Al-Hattami et al. 2024; Allami
et al. 2024; Almaqtari 2024; Almaqtari et al. 2022, 2024). Research suggests that AI greatly
improves accounting systems (Al-Hattami et al. 2024; Allami et al. 2024), auditing, and
operations (Abdullah and Almaqtari 2024) by automating routine tasks, enabling real-
time data processing and offering predictive analytics (Al-Hattami et al. 2024). Joshi et al.
(2018) indicate that IT governance frameworks enhance accountability and transparency by
enhancing the external reporting of relevant IT information to stakeholders, particularly in
strategic IT settings. Thus, this raises the importance of IT governance in AI integration,
as it aids in prioritizing investments and fostering innovation (Almaqtari 2024). Similarly,
Papagiannidis et al. (2023) highlight that AI governance is vital for effective decision-
making and overcoming implementation challenges. Rubino and Vitolla (2014) also argue
that IT governance is crucial for enterprise risk management and improving financial
outcomes. IT governance establishes unified data governance policies to ensure data
consistency and ethical use and defines ethical guidelines for deploying AI algorithms,
ensuring transparency, fairness, and accountability (Joshi et al. 2018; Papagiannidis et al.
2023; Rubino and Vitolla 2014; Wamba-Taguimdje et al. 2020). IT governance enforces data
governance frameworks to maintain data quality and accuracy, enhancing the reliability
of financial insights (Elazhary et al. 2022; Erasmus and Marnewick 2021; Sofyani et al.
2020). It also ensures compliance with regulatory standards in deploying AI and cloud
computing, adhering to legal requirements. The mediating role of IT governance ensures
a cohesive and responsible integration of AI and cloud computing into accounting and
auditing functions, contributing to more robust, accurate, and responsible financial and
audit practices.
AI in accounting and auditing offers numerous benefits, such as increased accuracy,
efficiency, and fraud detection skills. However, it also has drawbacks, such as high im-
plementation costs, data privacy issues, and regulatory compliance issues. Effective IT
governance is crucial for managing these problems and ensuring the successful integra-
tion of AI. IT governance frameworks help enterprises fully leverage AI while limiting
related risks by enabling strategy alignment, risk management, resource allocation, and
ethical monitoring. AI systems can process massive amounts of data with high preci-
sion, reducing the possibility of human error. They can automate repetitive processes,
allowing accountants and auditors to focus on more strategic work. Real-time data pro-
cessing improves the audit process’s responsiveness. Advanced predictive analytics use
past data to estimate future financial trends, assess risks, and find opportunities. Natural
language processing (NLP) enables AI to evaluate unstructured data, extracting essential
information and insights to aid audit processes. However, AI also has downsides, such
as high implementation costs, concerns about data privacy and security, complexity and
upkeep, legal and compliance issues, ethical considerations, and reliance on high-quality
data (Alreemy et al. 2016; Awwad and El Khoury 2021; Joshi et al. 2018). Effective IT gover-
nance ensures that AI investments align with the organization’s financial and operational
strategies, efficiently distribute resources, measure performance using metrics and KPIs,
and establish compliance and accountability systems (Awwad and El Khoury 2021; Caluwe
and De Haes 2019; Tallon et al. 2013; Vejseli et al. 2018). Thus, effective IT governance is
crucial for successfully integrating AI into accounting and auditing. It involves strategic
Economies 2024, 12, 199 18 of 24

alignment, risk management, resource allocation, performance measurement, compliance,

responsibility, change management, ethical concerns, and continuous improvement.
AI integration in accounting and auditing is a growing global phenomenon, with
firms worldwide using AI to improve their financial operations (Damerji and Salimi 2021;
Kopalle et al. 2022; Yoon 2020; S. Zhang et al. 2021). AI adoption in accounting and auditing
is diverse, with North America focusing on fraud detection, European countries focusing
on data privacy and regulatory compliance, Asia utilizing AI in fintech, the Middle East
investing in AI technologies, and Africa focusing on increasing financial inclusion and
combating fraud. Notable examples include Deloitte, PwC, EY, and KPMG in the US,
pioneering AI integration into audit procedures for risk assessment, fraud detection, and
predictive analytics. While Zhang et al. (2020), Tiberius and Hirth (2019), and Salijeni et al.
(2019) indicate that AI affects auditing practices in European countries, including Germany,
the UK, and France, Munoko et al. (2020) indicate that the European Commission’s Expert
Group on artificial intelligence emphasizes the need for AI-implementing firms to provide
stakeholders with clear, proactive information about AI system capabilities and limitations.
They all indicate that Europe, mainly the UK, Germany, and France, is adopting legal
frameworks to enable AI innovation while preserving data protection and ethical norms
(Munoko et al. 2020; Salijeni et al. 2019; Tiberius and Hirth 2019; Zhang et al. 2020).
Companies are also using AI to comply with regulations like GDPR, improving their
ability to manage big data volumes and maintain regulatory compliance (Zhang et al.
2020). Asia, particularly China and Japan, is investing in AI technologies to automate
operations (Kopalle et al. 2022; O’Leary 2009; Zhang et al. 2020). AI is not limited to
accounting and auditing functions in the private sector; the government sector also tries to
improve its operations through AI integration. For instance, the Australian government
is investing $124.1 million in an AI Action Plan to enhance its AI capabilities, while the
Hong Kong Department of Transport has been using AI for over a decade to collect and
classify cyber security information (Yigitcanlar et al. 2023). Thus, IT governance is crucial
in the modern government sector, especially in AI adoption. It ensures strategic alignment
with government goals, controls risks, promotes accountability, and protects data quality,
integrity, and security. It addresses ethical issues like spying and decision-making openness
(Abdullah and Almaqtari 2024; Al-Hattami et al. 2024; Allami et al. 2024; Almaqtari
2024; Almaqtari et al. 2022, 2024). These studies indicate that AI significantly enhances
accounting systems (Al-Hattami et al. 2024; Allami et al. 2024), audits, and operations
(Abdullah and Almaqtari 2024) by automating routine processes, enabling real-time data
processing, and providing predictive analytics (Al-Hattami et al. 2024). They also indicate
that IT governance is crucial for AI integration, facilitating investment prioritization and
innovation (Almaqtari 2024).
The study highlights the need for cultural adaptation and organizational change to
implement AI successfully. It suggests the need for educational and training programs
to include the skills needed to handle advanced technologies, highlighting a curriculum
gap. An interdisciplinary approach to academic education is also necessary, incorporating
aspects of information technology, graphic analysis, and management sciences. The study
also calls for developing educational and training programs aligned with the Kingdom’s
Vision 2030, which focuses on digital transformation and technological innovation. The
study also underscores the need for cooperation between academia and industry to develop
educational and training solutions based on actual practitioners’ needs, which aligns with
national development goals and Vision 2030. Moreover, the study adds to the literature
regarding the effects of technological changes on job roles in accounting and auditing
within the Kingdom. The results indicate the need to redefine and expand the scope of job
skills to include technical and analytical capabilities, which calls for updating academic
research to keep pace with these transformations and achieve the aspirations of Vision 2030.
This study provides practical insights for Saudi companies and institutions imple-
menting artificial intelligence, big data analytics, cloud computing, and deep learning
technologies in accounting and auditing functions. It emphasizes the need for cultural
Economies 2024, 12, 199 19 of 24

adaptation and organizational change management, as success depends on creating an

appropriate organizational and cultural environment. Companies should build advanced
technical capabilities within work teams through training and development programs,
including those dealing with artificial intelligence and big data analytics. Investments
in technical infrastructure, such as modernizing technological systems and using cloud
computing effectively, are crucial for supporting these transformations. Advanced technolo-
gies can enhance transparency and credibility in financial reporting, leading to economic
stability and confidence among investors and stakeholders. They can also provide new
job opportunities that require high technical skills, pushing the local workforce towards
specializations in line with the Kingdom’s Vision 2030 goal of building a diverse and
prosperous knowledge economy. These improvements can promote responsible and sus-
tainable business practices, such as improving auditing and fraud detection processes. The
study also highlights the need for regulatory policies that support technological innovation
while protecting data and privacy rights, requiring a careful balance between technologi-
cal progress and ethical and social considerations. Adopting advanced technologies can
contribute to achieving sustainable development goals and promote economic and social
growth in Saudi Arabia.

6. Conclusions
This paper investigates the mediating role of IT governance in the relationship be-
tween AI and accounting and auditing functions. Data were collected from various Saudi
organizations using convenience and snowball sampling methods, resulting in a final sam-
ple of 228 respondents. The findings reveal that IT governance significantly and positively
mediates the relationship between AI tools (big data, deep learning, and cloud computing)
and auditing functions (audit preparation and planning, process, and reporting). Similarly,
IT governance significantly mediates between AI and accounting operations (strategic
planning, reporting and taxation, and costing). This suggests that integrating AI, especially
big data, deep learning, and cloud computing, has substantially transformed accounting
and auditing functions. IT governance ensures these technologies’ ethical, secure, and
efficient use by establishing unified data governance policies, maintaining data quality and
security, and defining ethical guidelines for AI deployment to ensure transparency, fairness,
and accountability. It also enforces data governance frameworks to maintain quality and
accuracy standards and guides adherence to regulatory standards, ensuring responsible AI
and cloud computing deployment. Additionally, IT governance optimizes strategic plan-
ning, costing, budgeting, taxation, and auditing processes and includes risk management
strategies to identify, assess, and mitigate risks associated with AI and cloud computing.
The study aims to fill a gap in prior research by examining the adoption and imple-
mentation of AI in accounting and auditing practices. It also enhances understanding
of the role of IT governance in the relationship between AI and accounting and auditing
operations, making it a unique and novel contribution to the existing knowledge. The study
provides research-based perspectives from Saudi Arabia, supporting Saudi Vision 2030,
which promotes technical innovation and advancement in various industries, including
accounting and finance. By promoting the adoption of emerging technology, the study
contributes to achieving Vision 2030’s goals. It offers practical guidance for practitioners
and policymakers in incorporating cutting-edge technologies into accounting processes
and recommends governance mechanisms to maximize the advantages of AI and other
technologies. The findings could improve the effectiveness and capacity of accounting
operations, leading to better risk management, financial reporting, and overall organiza-
tional success. The study emphasizes the adoption of AI in Saudi accounting and auditing
functions, highlighting the crucial role of IT governance. It focuses on the role of AI tools in
transforming accounting operations. The findings have several practical, valuable insights
for practitioners, policymakers, and researchers, advancing the understanding of AI’s
effective integration and adoption in accounting operations. IT governance serves as an
alignment tool and approach.
Economies 2024, 12, 199 20 of 24

This study suggests that accounting and auditing experts can improve their func-
tions by incorporating AI tools, big data analytics, cloud computing, and deep learning
approaches. AI may improve efficiency, accuracy, and decision-making powers, automate
activities, decrease human error, and provide significant insights from large amounts of
data, leading to enhanced audit quality and better services. Policymakers can use data to
establish supportive frameworks for innovation and sustainability, including incentives,
infrastructure, legal support, and professional development programs. These strategies can
accelerate progress, foster a culture of continuous improvement, and ensure long-term eco-
nomic and social benefits. The current study also educates policymakers and professionals
that IT governance facilitates and mitigates these complexities’ adverse effects while there
is complexity in using AI tools in accounting functions.
Despite the critical findings of the current study, it has certain limitations. First, it is
limited to an emerging country, Saudi Arabia, and its global generalizability is restricted
due to several factors, including regulations, infrastructure, culture, and specific market
dynamics. Future research should look at a broader population from several countries and
industries to better understand the variations in AI implementation. Second, the survey
could have offered comprehensive insights into the challenges, limitations, and factors
determining AI’s successful implementation and adoption in accounting and auditing
functions. Third, the research should have addressed the unique challenges that small-
and medium-sized enterprises (SMEs) encounter when implementing AI technologies.
Finally, the influence of AI on the accounting and auditing workforce still needs to be
addressed. Thus, a possible stream for future research is the exploration of the challenges
that industries and SMEs encounter when implementing AI technologies, including the
skills needed for future generations.

Funding: No funding has been provided for this manuscript.

Informed Consent Statement: Not applicable.
Data Availability Statement: Data will be made available upon request.
Conflicts of Interest: The author declares no conflicts of interest.

References
Abdullah, Abdulwahid Ahmad Hashed, and Faozi A. Almaqtari. 2024. The impact of artificial intelligence and Industry 4.0 on
transforming accounting and auditing practices. Journal of Open Innovation: Technology, Market, and Complexity 10: 100218.
[CrossRef]
Abu-Musa, Ahmad A. 2008. Information technology and its implications for internal auditing: An empirical study of Saudi organiza-
tions. Managerial Auditing Journal 23: 438–66. [CrossRef]
Al Omari, Loai. 2016. IT Governance Evaluation: Adapting and Adopting the COBIT Framework for Public Sector Organisations.
Doctoral dissertation, Queensland University of Technology, Brisbane, Australia.
Alanazi, Fayez. 2023. Development of Smart Mobility Infrastructure in Saudi Arabia: A Benchmarking Approach. Sustainability
15: 3158. [CrossRef]
Al-Baity, Heyam H. 2023. The artificial intelligence revolution in digital finance in Saudi Arabia: A comprehensive review and proposed
framework. Sustainability 15: 13725. [CrossRef]
AlGhazzawi, Nooradeen Adel. 2020. Adoption of Cloud Enterprise Resource Planning Systems (CERPs) in the Kingdom of Saudi
Arabia. Doctoral dissertation, RMIT University, Melbourne, Australia.
Al-Hattami, Hamood Mohammed. 2023. Understanding perceptions of academics toward technology acceptance in accounting
education. Heliyon 9: e13141. [CrossRef] [PubMed]
Al-Hattami, Hamood Mohammed, and Faozi A. Almaqtari. 2023. What determines digital accounting systems’ continuance intention?
An empirical investigation in SMEs. Humanities and Social Sciences Communications 10: 1–13. [CrossRef]
Al-Hattami, Hamood Mohammed, Faozi A. Almaqtari, Abdulwahid Ahmed Hashed Abdullah, and Ahmad Samed Al-Adwan. 2024.
Digital accounting system and its effect on corporate governance: An empirical investigation. Strategic Change 33: 151–67.
[CrossRef]
Al-Htaybat, Khaldoon, Larissa von Alberti-Alhtaybat, and Zaidoon Alhatabat. 2018. Educating digital natives for the future:
Accounting educators’ evaluation of the accounting curriculum. Accounting Education 27: 333–57. [CrossRef]
Alhumoudi, Hamad, and Abdulrahman Juayr. 2023. Exploring the impact of artificial intelligence and digital transformation on
auditing practices in Saudi Arabia: A cross-sectional study. Asian Journal of Finance and Accounting 15: 1–30. [CrossRef]
Economies 2024, 12, 199 21 of 24

Allami, Karrar Khalaf Jabbar, Faozi A. Almaqtari, Hamood Mohammed Al-Hattami, and Ritu Sapra. 2024. Factors associated with the
intention to use information technology in audit in Iraq. Information Discovery and Delivery 52: 197–212. [CrossRef]
Alles, Michael, and Glen L. Gray. 2016. Incorporating big data in audits: Identifying inhibitors and a research agenda to address those
inhibitors. International Journal of Accounting Information Systems 22: 44–59. [CrossRef]
Almaqtari, Faozi A. 2024. The moderating role of IT governance on the relationship between FinTech and sustainability performance.
Journal of Open Innovation: Technology, Market, and Complexity 10: 100267. [CrossRef]
Almaqtari, Faozi A., Najib HS Farhan, Ali T. Yahya, Borhan Omar Ahmad Al-Dalaien, and Mohd Shamim. 2022. The mediating effect
of IT governance between corporate governance mechanisms, business continuity, and transparency and disclosure: An empirical
study of COVID-19 Pandemic in Jordan. Information Security Journal 1: 1–19. [CrossRef]
Almaqtari, Faozi A., Najib HS Farhan, Hamood Mohammed Al-Hattami, Tamer Elsheikh, and Borhan Omar Ahmad Al-dalaien. 2024.
The Impact of Artificial Intelligence on Information Audit Usage: Evidence from Developing Countries. Journal of Open Innovation:
Technology, Market, and Complexity 10: 100298. [CrossRef]
Alotaibi, Nayef Shaie, and Awad Hajran Alshehri. 2023. Prospers and obstacles in using artificial intelligence in Saudi Arabia higher
education institutions—The potential of AI-based learning outcomes. Sustainability 15: 10723. [CrossRef]
Alreemy, Zyad, Victor Chang, Robert Walters, and Gary Wills. 2016. Critical success factors (CSFs) for information technology
governance (ITG). International Journal of Information Management 36: 907–16. [CrossRef]
Alzahrani, Reema Bakheet. 2024. An Overview of AI Data Protection in the Context of Saudi Arabia. International Journal for Scientific
Research 3: 1–20. [CrossRef]
Aqlan, Saleem Ahmed Ghaleb. 2021. The Role of Accounting Data and Information in Rationalization of Managerial Decisions in
Private Industrial Units in the Republic of Yemen. Doctoral dissertation, Dr. Babasaheb Ambedkar Marathwada University,
Aurangabad, India.
Arnaboldi, Michela, Cristiano Busco, and Suresh Cuganesan. 2017. Accounting, accountability, social media and big data: Revolution
or hype? Accounting, Auditing and Accountability Journal 30: 762–76. [CrossRef]
Awwad, Bahaa, and Rim El Khoury. 2021. Information technology governance and bank performance: Evidence from Palestine. Journal
of Decision Systems 33: 311–34. [CrossRef]
Bradley, Joseph M., and Ralph T. Soule. 2018. Information Technology Governance through the Complex System Governance Lens.
INCOSE International Symposium 28: 1237–49. [CrossRef]
Brown-Liburd, Helen, and Miklos A. Vasarhelyi. 2015. Big data and audit evidence. Journal of Emerging Technologies in Accounting 12:
1–16. [CrossRef]
Cabrera-Sánchez, Juan-Pedro, Ángel F. Villarejo-Ramos, Francisco Liébana-Cabanillas, and Aijaz A. Shaikh. 2021. Identifying relevant
segments of AI applications adopters—Expanding the UTAUT2′ s variables. Telematics and Informatics 58: 101529. [CrossRef]
Caluwe, Laura, and Steven De Haes. 2019. Board Level IT Governance: A Scoping Review to Set the Research Agenda. Information
Systems Management 36: 262–83. [CrossRef]
Cath, Corinne. 2018. Governing artificial intelligence: Ethical, legal and technical opportunities and challenges. Philosophical Transactions
of the Royal Society A: Mathematical, Physical and Engineering Sciences 376: 20180080. [CrossRef] [PubMed]
Chen, Shu. 2021. The Impact of Artificial Intelligence and Data Fusion Technology on the Accounting Industry and Its Countermeasures.
In ACM International Conference Proceeding Series. New York: Association for Computing Machinery, pp. 877–81. [CrossRef]
Cockcroft, Sophie, and Mark Russell. 2018. Big Data Opportunities for Accounting and Finance Practice and Research. Australian
Accounting Review 28: 323–33. [CrossRef]
Damerji, Hassan, and Anwar Salimi. 2021. Mediating effect of use perceptions on technology readiness and adoption of artificial
intelligence in accounting. Accounting Education 30: 107–30. [CrossRef]
Deloitte. 2023. New Fundamentals for a Boundaryless World Middle East 2023 Human Capital Trends. Available online: https://www2
.deloitte.com/content/dam/Deloitte/xe/Documents/human-capital/2023-Middle-East-Human-Capital-Trends-Report.pdf (ac-
cessed on 23 February 2024).
Dhamija, Pavitra, and Surajit Bag. 2020. Role of artificial intelligence in operations environment: A review and bibliometric analysis.
The TQM Journal 32: 869–96. [CrossRef]
Di Vaio, Assunta, Rosa Palladino, Rohail Hassan, and Octavio Escobar. 2020. Artificial intelligence and business models in the
sustainable development goals perspective: A systematic literature review. Journal of Business Research 121: 283–314. [CrossRef]
Earley, Christine E. 2015. Data analytics in auditing: Opportunities and challenges. Business Horizons 58: 493–500. [CrossRef]
Elazhary, Moustafa, Aleš Popovič, Paulo Henrique de Souza Bermejo, and Tiago Oliveira. 2022. How Information Technology
Governance Influences Organizational Agility: The Role of Market Turbulence. Information Systems Management 40: 148–68.
[CrossRef]
Enholm, Ida Merete, Patrick Mikalef, Emmanouil Papagiannidis, and John Krogstie. 2022. Artificial intelligence and business value:
A literature review. Information Systems Frontiers 24: 1709–34. [CrossRef]
Erasmus, Wikus, and Carl Marnewick. 2021. An IT governance framework for IS portfolio management. International Journal of
Managing Projects in Business 14: 721–42. [CrossRef]
Faccia, Alessio, Mohamed Yousif Khamis Al Naqbi, and Saeed Ahmad Lootah. 2019. Integrated Cloud Financial Accounting Cycle.
Paper presented at the 2019 3rd International Conference on Cloud and Big Data Computing, Oxford, UK, August 28–30; pp.
31–37. [CrossRef]
Economies 2024, 12, 199 22 of 24

Floridi, Luciano. 2018. Soft Ethics and the Governance of the Digital. Philosophy and Technology 31: 1–8. [CrossRef]
Gomez, Javier. 2018. What should i wear today? An IoT–Based dress assistant for the E–Society. In Lecture Notes in Computer Science
(Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). LNCS; Cham: Springer, vol. 11195.
[CrossRef]
Gotthardt, Max, Dan Koivulaakso, Okyanus Paksoy, Cornelius Saramo, Minna Martikainen, and Othmar Lehner. 2020. Current state
and challenges in the implementation of smart robotic process automation in accounting and auditing. ACRN Journal of Finance
and Risk Perspectives 9: 90–102. [CrossRef]
Groomer, S. Michael, and Uday S. Murthy. 2018. Continuous Auditing of Database Applications: An Embedded Audit Module
Approach. In Continuous Auditing (Rutgers Studies in Accounting Analytics). Edited by David Y. Chan, Victoria Chiu and Miklos A.
Vasarhelyi. Leeds: Emerald Publishing Limited, pp. 105–24. [CrossRef]
Hamza, Randa Abd Elhamied Mohammed, Nasareldeen Hamed Ahmed, Abdelwhab Musa Elgali Mohamed, Mohamed Youcef
Bennaceur, Ahmed Hesham Moawed Elhefni, and Mona M. Elshaabany. 2024. The Impact of Artificial Intelligence (AI) on the
Accounting System of Saudi Companies. WSEAS Transactions on Business and Economics 21: 499–511. [CrossRef]
Han, Hongdan, Radha K. Shiwakoti, Robin Jarvis, Chima Mordi, and David Botchie. 2023. Accounting and auditing with blockchain
technology and artificial Intelligence: A literature review. International Journal of Accounting Information Systems 48: 100598.
[CrossRef]
Handoko, Bambang Leo, and Stephanie Liusman. 2021. Analysis of External Auditor Intentions in Adopting Artificial Intelligence as
Fraud Detection with the Unified Theory of Acceptance and Use of Technology (UTAUT) Approach. Paper presented at the 2021
12th International Conference on E-business, Management and Economics, Beijing, China, July 17–19; pp. 96–103.
Hardin-Ramanan, Sarita, Vanessa Chang, and Tomayess Issa. 2018. A Green Information Technology governance model for large
Mauritian companies. Journal of Cleaner Production 198: 488–97. [CrossRef]
Hooda, Nishtha, Seema Bawa, and Prashant Singh Rana. 2020. Optimizing Fraudulent Firm Prediction Using Ensemble Machine
Learning: A Case Study of an External Audit. Applied Artificial Intelligence 34: 20–30. [CrossRef]
Hu, Li-tze, and Peter M. Bentler. 1998. Fit indices in covariance structure modeling: Sensitivity to underparameterized model
misspecification. Psychological Methods 3: 424–53. [CrossRef]
Hu, Xiao, Rita Yi Man Li, Kalpina Kumari, Samira Ben Belgacem, Qinghua Fu, Mohammed Arshad Khan, and Abdulaziz A.
Alkhuraydili. 2022. Relationship between green leaders’ emotional intelligence and employees’ green behavior: A PLS-SEM
approach. Behavioral Sciences 13: 25. [CrossRef] [PubMed]
Huerta, Esperanza, and Scott Jensen. 2017. An accounting information systems perspective on data analytics and big data. Journal of
Information Systems 31: 101–14. [CrossRef]
Issa, Hussein, Ting Sun, and Miklos A. Vasarhelyi. 2016. Research ideas for artificial intelligence in auditing: The formalization of audit
and workforce supplementation. Journal of Emerging Technologies in Accounting 13: 1–20. [CrossRef]
Jain, S. 2018. US $320 Billion by 2030? The Potential Impact of AI in the Middle East, PwC. Available online: https://www.pwc.com/
m1/en/publications/potential-impact-artificial-intelligence-middle-east.html#:~:text=US$320%20billion.-,In%20absolute%20
terms,%20the%20largest%20gains%20are%20expected%20to%20accrue,to%2014%25%20of%202030%20GDP (accessed on 23
February 2024).
Joshi, Anant, Laury Bollen, Harold Hassink, Steven De Haes, and Wim Van Grembergen. 2018. Explaining IT governance disclosure
through the constructs of IT governance maturity and IT strategic role. Information and Management 55: 368–80. [CrossRef]
Khan, Mohammed Arshad, Maysoon Khojah, and Vivek. 2022. Artificial intelligence and big data: The advent of new pedagogy in the
adaptive e-learning system in the higher educational institutions of Saudi Arabia. Education Research International 2022: 1263555.
[CrossRef]
Kokina, Julia, and Thomas H. Davenport. 2017. The emergence of artificial intelligence: How automation is changing auditing. Journal
of Emerging Technologies in Accounting 14: 115–22. [CrossRef]
Kopalle, Praveen K., Manish Gangwar, Andreas Kaplan, Divya Ramachandran, Werner Reinartz, and Aric Rindfleisch. 2022. Exam-
ining artificial intelligence (AI) technologies in marketing via a global lens: Current trends and future research opportunities.
International Journal of Research in Marketing 39: 522–40. [CrossRef]
Kostka, Genia, Xuehua Zhang, and Kyoung Shin. 2020. Information, technology, and digitalization in China’s environmental
governance. Journal of Environmental Planning and Management 63: 1–13. [CrossRef]
Krichene, Aida, and Emna Baklouti. 2020. Internal audit quality: Perceptions of Tunisian internal auditors an explanatory research.
Journal of Financial Reporting and Accounting 19: 28–54. [CrossRef]
Lee, Cheah Saw, and Farzana Parveen Tajudeen. 2020. Usage and impact of artificial intelligence on accounting: Evidence from
Malaysian organisations. Asian Journal of Business and Accounting 13: 213–39. [CrossRef]
Li, Junying, Jirawan Deeprasert, Rita Yi Man Li, and Wei Lu. 2022. The Influence of Chinese Professional Basketball Organiza-
tions’(CPBOs’) Corporate Social Responsibility (CSR) Efforts on Their Clubs’ Sustainable Development. Sustainability 14: 12339.
[CrossRef]
Losbichler, Heimo, and Othmar M. Lehner. 2021. Limits of artificial intelligence in controlling and the ways forward: A call for future
accounting research. Journal of Applied Accounting Research 22: 365–82. [CrossRef]
Mikalef, Patrick, and Manjul Gupta. 2021. Artificial intelligence capability: Conceptualization, measurement calibration, and empirical
study on its impact on organizational creativity and firm performance. Information and Management 58: 103434. [CrossRef]
Economies 2024, 12, 199 23 of 24

Molla, Alemayehu, Vanessa A. Cooper, and Siddhi Pittayachawan. 2009. IT and Eco-Sustainability: Developing and Validating a Green
IT Readiness Model. ICIS 2009 Proceedings. vol. 141, pp. 1–21. Available online: http://aisel.aisnet.org/icis2009/141/ (accessed
on 23 February 2024).
Munoko, Ivy, Helen L. Brown-Liburd, and Miklos Vasarhelyi. 2020. The Ethical Implications of Using Artificial Intelligence in Auditing.
Journal of Business Ethics 167: 209–34. [CrossRef]
Nedbal, Dietmar, Werner Wetzlinger, Andreas Auinger, and Gerold Wagner. 2011. Sustainable IS Initialization through Outsourcing:
A Theory-Based Approach. pp. 1–10. Available online: https://aisel.aisnet.org/amcis2011_submissions/255 (accessed on 23
February 2024).
Noor, Nor Raihana Asmar Mohd, and Noorhayati Mansor. 2019. Exploring the Adaptation of Artificial Intelligence in Whistleblowing
Practice of the Internal Auditors in Malaysia. Procedia Computer Science 163: 434–39. [CrossRef]
O’Leary, Daniel E. 2009. Downloads and citations in Intelligent Systems in Accounting, Finance and Management. Intelligent Systems in
Accounting, Finance and Management 16: 21–31. [CrossRef]
Papagiannidis, Emmanouil, Ida Merete Enholm, Chirstian Dremel, Patrick Mikalef, and John Krogstie. 2023. Toward AI Governance:
Identifying Best Practices and Potential Barriers and Outcomes. Information Systems Frontiers 25: 123–41. [CrossRef] [PubMed]
Prakash, Gyan, and Kumar Ambedkar. 2022. Digitalization of manufacturing for implanting value, configuring circularity and
achieving sustainability. Journal of Advances in Management Research 20: 116–39. [CrossRef]
Rahman, Noorul Shaiful Fitri Abdul, Abdelsalam Adam Hamid, Taih-Cherng Lirn, Khalid Al Kalbani, and Bekir Sahin. 2022. The
adoption of industry 4.0 practices by the logistics industry: A systematic review of the Gulf region. Cleaner Logistics and Supply
Chain 5: 100085. [CrossRef]
Razi, Muhammad A., and Haider H. Madani. 2013. An analysis of attributes that impact adoption of audit software. International
Journal of Accounting and Information Management 21: 170–88. [CrossRef]
Rubino, Michele, and Filippo Vitolla. 2014. Corporate governance and the information system: How a framework for IT governance
supports ERM. Corporate Governance 14: 320–38. [CrossRef]
Salijeni, George, Anna Samsonova-Taddei, and Stuart Turley. 2019. Big Data and changes in audit technology: Contemplating a
research agenda. Accounting and Business Research 49: 95–119. [CrossRef]
Savtschenko, Maximilian, Frederik Schulte, and Stefan Voß. 2017. It governance for cyber-physical systems: The case of industry 4.0. In
Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). LNCS;
Cham: Springer, vol. 10288, pp. 667–76. [CrossRef]
Schmidt, Nils-Holger, and Lutz Kolbe. 2011. Towards a Contingency Model for Green IT Governance. Available online: https:
//aisel.aisnet.org/ecis2011/105 (accessed on 7 February 2024).
Schmitz, Jana, and Giulia Leoni. 2019. Accounting and Auditing at the Time of Blockchain Technology: A Research Agenda. Australian
Accounting Review 29: 331–42. [CrossRef]
Shaffer, Kathie J., Carol J. Gaumer, and Kiersten P. Bradley. 2020. Artificial intelligence products reshape accounting: Time to re-train.
Development and Learning in Organizations 34: 41–43. [CrossRef]
Simonsson, Mårten, Pontus Johnson, and Mathias Ekstedt. 2010. The effect of IT governance maturity on IT governance performance.
Information Systems Management 27: 10–24. [CrossRef]
Sledgianowski, Deb, Mohamed Gomaa, and Christine Tan. 2017. Toward integration of Big Data, technology and information systems
competencies into the accounting curriculum. Journal of Accounting Education 38: 81–93. [CrossRef]
Sofyani, Hafiez, Hosam Alden Riyadh, and Heru Fahlevi. 2020. Improving service quality, accountability and transparency of local
government: The intervening role of information technology governance. Cogent Business and Management 7: 1735690. [CrossRef]
Sun, Ting, and Miklos A. Vasarhelyi. 2018. Embracing textual data analytics in auditing with deep learning. International Journal of
Digital Accounting Research 18: 49–67. [CrossRef] [PubMed]
Sutton, Steve G., Matthew Holt, and Vicky Arnold. 2016. “The reports of my death are greatly exaggerated”—Artificial intelligence
research in accounting. International Journal of Accounting Information Systems 22: 60–73. [CrossRef]
Tallon, Paul P., Ronald V. Ramirez, and James E. Short. 2013. The information artifact in IT governance: Toward a theory of information
governance. Journal of Management Information Systems 30: 141–78. [CrossRef]
Teixeira, Josélia Elvira, and Ana Teresa Tavares-Lehmann. 2022. Industry 4.0: The future of manufacturing from the perspective of
business and economics—A bibliometric literature review. Competitiveness Review 33: 458–82. [CrossRef]
The Ministry of Finance. 2024. Budget Statement Fiscal Year 2024. Available online: https://www.mof.gov.sa/en/budget/2024
/Documents/Bud-E%202024%20F4.pdf (accessed on 7 February 2024).
Tiberius, Victor, and Stefanie Hirth. 2019. Impacts of digitization on auditing: A Delphi study for Germany. Journal of International
Accounting, Auditing and Taxation 37: 100288. [CrossRef]
Turel, Ofir, Peng Liu, and Chris Bart. 2017. Board-Level Information Technology Governance Effects on Organizational Performance:
The Roles of Strategic Alignment and Authoritarian Governance Style. Information Systems Management 34: 117–36. [CrossRef]
Vărzaru, Anca Antoaneta. 2022. Assessing Artificial Intelligence Technology Acceptance in Managerial Accounting. Electronics 11: 2256.
[CrossRef]
Economies 2024, 12, 199 24 of 24

Vejseli, Sulejman, Daniel Proba, Alexander Rossmann, and Reinhard Jung. 2018. The agile strategies in IT governance: Towards
a framework of agile IT governance in the banking industry. Paper presented at the Twenty-Sixth European Conference on
Information Systems (ECIS2018), Portsmouth, UK, June 23–28; pp. 1–17. Available online: https://aisel.aisnet.org/ecis2018_rp/
148 (accessed on 28 November 2018).
Wamba-Taguimdje, Serge-Lopez, Samuel Fosso Wamba, Jean Robert Kala Kamdjoug, and Chris Emmanuel Tchatchouang Wanko. 2020.
Influence of artificial intelligence (AI) on firm performance: The business value of AI-based transformation projects. Business
Process Management Journal 26: 1893–924. [CrossRef]
Wang, Tao. 2022. Board human capital diversity and corporate innovation: A longitudinal study. Corporate Governance: The International
Journal of Business in Society 22: 680–701. [CrossRef]
Warren, J. Donald, Kevin C. Moffitt, and Paul Byrnes. 2015. How big data will change accounting. Accounting Horizons 29: 397–407.
[CrossRef]
Wilkin, Carla L., Paul K. Couchman, Amrik Sohal, and Ambika Zutshi. 2016. Exploring differences between smaller and large
organizations’ corporate governance of information technology. International Journal of Accounting Information Systems 22: 6–25.
[CrossRef]
Winfield, Alan F., Katina Michael, Jeremy Pitt, and Vanessa Evers. 2019. Machine ethics: The design and governance of ethical ai and
autonomous systems. Proceedings of the IEEE 107: 509–17. [CrossRef]
Xia, Huosong, Duqun Lu, Boqiang Lin, Jeretta Horn Nord, and Justin Zuopeng Zhang. 2022. Trust in Fintech: Risk, Governance, and
Continuance Intention. Journal of Computer Information Systems 63: 648–62. [CrossRef]
Yigitcanlar, Tan, Rita Yi Man Li, Prithvi Bhat Beeramoole, and Alexander Paz. 2023. Artificial intelligence in local government services:
Public perceptions from Australia and Hong Kong. Government Information Quarterly 40: 101833. [CrossRef]
Yoon, Sora. 2020. A study on the transformation of accounting based on new technologies: Evidence from Korea. Sustainability 12: 8669.
[CrossRef]
Yu, Songbo, Jaffar Abbas, Anca Draghici, Oriana Helena Negulescu, and Noor Ul Ain. 2022. Social Media Application as a New
Paradigm for Business Communication: The Role of COVID-19 Knowledge, Social Distancing, and Preventive Attitudes. Frontiers
in Psychology 13: 903082. [CrossRef] [PubMed]
Zhang, Shiying, Zixuan Meng, Beibei Chen, Xiu Yang, and Xinran Zhao. 2021. Motivation, Social Emotion, and the Acceptance of
Artificial Intelligence Virtual Assistants—Trust-Based Mediating Effects. Frontiers in Psychology 12: 728495. [CrossRef]
Zhang, Yingying, Feng Xiong, Yi Xie, Xuan Fan, and Haifeng Gu. 2020. The impact of artificial intelligence and blockchain on the
accounting profession. IEEE Access 8: 110461–477. [CrossRef]

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual
author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to
people or property resulting from any ideas, methods, instructions or products referred to in the content.