Journal of Open Innovation: Technology, Market, and Complexity 10 (2024) 100218

Available online 22 January 2024

(http://creativecommons.org/licenses/by-nc-nd/4.0/).

The impact of articial intelligence and Industry 4.0 on transforming

accounting and auditing practices

Abdulwahid Ahmad Hashed Abdullah

, Faozi A. Almaqtari

Department of Accounting, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia

Department of Accounting and Finance, College of Business Administration, A’Sharqiyah University (ASU), Ibra, Oman

ARTICLE INFO

JEL Classications:

M410

M420

M490

Keywords:

Articial Intelligence

Industry 4.0 readiness

Technology acceptance model

Accounting education

Auditing practices

Accounting practices

ABSTRACT

The main aim is to investigate the impact of articial intelligence (AI), Industry 4.0 readiness, and Technology

Acceptance Model (TAM) variables on various aspects of accounting and auditing operations. To evaluate the

associations between the variables, the research design employs a mediation and path approach using SMART

PLS. The study employs a convenience sampling method, which is augmented with snowball sampling. The

sample size was determined using various techniques, yielding a nal sample of 228 respondents. The ndings

indicate that leveraging AI, big data analytics, cloud computing, and deep learning advancements can improve

accounting and auditing practices. AI technologies assist businesses in increasing their efciency, accuracy, and

decision-making capabilities, resulting in improved nancial reporting and auditing processes. The study con-

tributes to the theoretical explanation of the inuence of AI adoption in accounting and auditing practices in the

context of an emerging country, Saudi Arabia. The ndings of the study have practical implications for ac-

counting and auditing practitioners, policymakers, and scholars. The ndings of this study can assist businesses

in efciently leveraging AI developments to improve their accounting and auditing operations. Policymakers can

use the ndings to create supporting frameworks and regulations that encourage the adoption and integration of

articial intelligence in the domain. These ndings contribute to the existing stock of knowledge on the use of AI

in accounting and auditing, as well as providing evidence of its benets in the context of an emerging country.

Introduction

Accounting and auditing are critical roles that ensure the reliability,

credibility, and nancial stability of a corporation. They provide

objective assurance and contribute to market trust, making them

essential to the overall health of the economy (Feliciano and Quick,

2022; Al-Hattami et al., 2021). Historically, these functions were mostly

dependent on manual processes and human expertise. However, as in-

formation technology (IT) has grown, a paradigm shift in the way ac-

counting and auditing are handled has occurred. Recognizing the

relevance of IT in this context, organizations, accountants, auditors,

professional bodies, academics, and regulators have switched their focus

to improving auditing and accounting processes using technology

(Al-Hattami, 2022; Al-Hattami, 2023).

Curtis and Payne (2008) indicate that the use of technology,

particularly information technology, has proven crucial for boosting

audit quality and efciency. It has a number of advantages, including

enhanced dependability, productivity, efciency, and decreased audit

costs (Correia et al., 2020). The use of IT also saves time in audit tasks,

which allows auditors to assign their efforts more effectively (Thottoli

et al., 2022). Thus, the signicant effect of technology on auditing is

obvious, as it has become practically difcult to conduct an effective

audit without embracing IT (Al-Hattami et al., 2021; Al-Hattami, 2021;

Thottoli et al., 2022). Hence, with modern technologies such as Articial

Intelligence (AI) and Industry 4.0, the discipline of accounting and

auditing has been developed tremendously (Jamwal et al., 2021;

Enholm et al., 2022; Polak, 2021; Munoko et al., 2020; Han et al., 2023;

Al-Sayyed et al., 2021). This has been conrmed by several studies (Lasi

et al., 2014; Tjahjono et al., 2017; Jamwal et al., 2021; Loureiro et al.,

2021; Enholm et al., 2022; Polak, 2021). Besides, technology integration

in accounting procedures has also been identied as a technique to

improve management control effectiveness (Al-Hattami and Kabra,

* Corresponding author.

E-mail addresses: fouzi_gazim2005@yahoo.com, faozi.almaqtari@asu.edu.om (F.A. Almaqtari).

https://orcid.org/0000-0002-6791-235X

ORCID.ID/0000–0002-5625–3643

Contents lists available at ScienceDirect

Journal of Open Innovation: Technology, Market,

and Complexity

journal homepage: www.sciencedirect.com/journal/journal-of-open-innovation-technology-

market-and-complexity

https://doi.org/10.1016/j.joitmc.2024.100218

Received 22 October 2023; Received in revised form 12 January 2024; Accepted 17 January 2024

Journal of Open Innovation: Technology, Market, and Complexity 10 (2024) 100218

2022).

The rise of AI has signicantly transformed the accounting profes-

sion, enhancing accuracy and data quality (Bin-Abbas and Bakry, 2014;

Erasmus and Marnewick, 2021; Manita et al., 2020; Papagiannidis et al.,

2022; Rezaee et al., 2018; Rushinek and Rushinek, 1989; Tsai et al.,

2015; Willson and Pollard, 2009). Flowerday et al. (2006), Sledgia-

nowski et al. (2017), Tiberius and Hirth (2019) reported that the new

challenges that are becoming more prevalent necessitate innovation in

accounting practices. Key links to innovation include automation of

routine tasks, improved accuracy, real-time reporting, data analytics,

cybersecurity challenges, continuous learning, ethical considerations,

global standardization, and emerging roles (Baldwin et al., 2006; Curtis

and Payne, 2014; Tiberius and Hirth, 2019). In this context, accounting

rms can efciently utilize AI by automating repetitive tasks, imple-

menting predictive analytics, incorporating AI tools, auditing proced-

ures, blockchain technology, and AI-powered chatbots. Therefore,

accounting practices can improve efciency, accuracy, and strategic

decision-making through modern technologies and leveraging Industry

4.0 capabilities (Burritt and Christ, 2016; Cervone, 2017; Di Vaio et al.,

2020; Ghobakhloo, 2018; Sirisomboonsuk et al., 2018).

From the argument above, it is obvious that technology, particularly

AI (Munoko et al., 2020; Han et al., 2023; Al-Sayyed et al., 2021; Zhang

et al., 2020) and Industry 4.0 (Tavares et al., 2022; Fül

op et al., 2022;

Ozcan and Akkaya, 2020), have signicantly impacted accounting and

auditing worldwide. The use of these technologies has become critical to

the efcacy and efciency of accounting and auditing operations.

Studies (i.e., ACCA, 2019; Curtis and Payne, 2008; Correia et al., 2020;

Thottoli et al., 2022) support the view that IT has the potential to

improve audit quality, dependability, productivity, cost reduction, and

time savings on audit duties. Besides, the integration of AI, machine

learning, deep learning, big data analytics, data mining, and cloud

computing into accounting and auditing processes enables the analysis

of large volumes of nancial data, the identication of patterns, trends,

and anomalies, and improved decision-making (Coman et al., 2022;

Lehner et al., 2022; Yoon, 2020).

While the use of technology in accounting and auditing has been

extensively researched in developed countries, research in developing

countries, such as Saudi Arabia, is scarce (Al-Hattami et al., 2021;

Al-Mohammedi, 2020). For example, China’s Industry 4.0 development

is heavily reliant on the integration of Industry 4.0 and improved arti-

cial intelligence performance. Furthermore, the development of

China’s Industry 4.0 is dependent on delivering a high level of articial

intelligence, the requisite nancial resources, the formation of high level

and advanced industrial zones, the enhancement of production pro-

cesses, and research collaborations. As a result, advanced Industry 4.0

with smart production is achieved (Hou et al., 2020).

Saudi Arabia has made signicant investments in new technologies

such as articial intelligence, 5 G, and data management (Alanazi, 2023;

Ghazwani et al., 2022; Khan et al., 2022). These investments have aided

the country’s position as a leader in these cutting-edge industries, pro-

moting economic growth. Technology is also a crucial and key topic in

Saudi Arabia’s Vision 2030, with digital transformation plans multi-

plying in recent years (Hassan, 2020; Alanazi, 2023; Dhar Dwivedi et al.,

2021). The Saudi government also places emphasis on cybersecurity,

coding, articial intelligence, and gaming (Sairete et al., 2021). Ac-

cording to Sairete et al. (2021), the PwC consulting rm, the world’s

second-largest professional services network, AI might contribute $135

billion, or 12.4%, to Saudi Arabia’s GDP by 2030. One of the funda-

mental themes of the Saudi Vision 2030 is that AI has created a pool of

opportunity for digital transformation and creative services. Thus, there

are notable governmental efforts in Saudi Arabia toward digital trans-

formation; however, the readiness of Saudi businesses for this digital

transformation needs to be explored in terms of the factors inuencing

their readiness and the potential challenges that may arise. To that end,

the purpose of this study is to ll a research gap in the literature. As a

result, the purpose of this research is to investigate the impact of AI and

Industry 4.0 readiness on accounting and auditing processes in Saudi

Arabia. This research intends to provide signicant insights into the

factors, obstacles, and benets of adopting and employing technology in

accounting and auditing in the country by focusing on the specic

context of Saudi Arabia.

This research contributes to the audit literature by addressing some

research gaps. First, it is one of the rst studies to study the factors

inuencing the intention to adopt and use emerging audit technology in

Saudi Arabia, bringing insights to the setting of a developing country.

This study broadens our understanding of the adoption and utilization of

emerging technologies by taking into account the country-specic fac-

tors at play by focusing on Saudi Arabia. Second, this study adds to our

understanding of the factors that inuence the adoption and use of

developing audit technology. This study contributes to our under-

standing of the critical elements driving the adoption of these technol-

ogies by developing and empirically testing a theoretical model. This

study’s empirical evidence and insights can help practitioners, policy-

makers, and researchers in Saudi Arabia develop strategies and policies

to promote the adoption and successful use of emerging technologies in

accounting and auditing. Third, the current study incorporates the TAM

model in order to evaluate the readiness and intention to use AI and

Industry 4.0 readiness in accounting and auditing operations. Finally,

the current study could serve as a paradigm for future research in

merging nations, particularly those in the Gulf region and Arab countries

with comparable cultures and views.

The present research is structured as follows: The research hypoth-

eses are developed in Section 2. Section 3 discusses the methodology.

Section 4 provides the data analysis and discussion. Section 5 contains

additional analysis. Section 6 discusses the results and highlights the

research implications, and section 7 summarizes the result and discusses

the study’s limitations as well as future research.

Research model and hypothesis development

Technology acceptance model

The rst Technology Acceptance Model (TAM) was proposed by

Davis et al. (1989). The model has been regularly employed by re-

searchers to assess the users’ acceptability of information technologies

(IT) (Usman et al., 2022; Qasim, Kharbat, 2020; Pedrosa et al., 2020;

arzaru, 2022). The model is characterised by its ease of use and

perceived usefulness: two variables that inuence users’ intention to use

IT systems. Previous literature indicates that this model is a

well-recognised model, which has been widely acknowledged to provide

insights into the level of acceptance and adoption of IT systems (King &

He, 2006; Usman et al., 2022; V

arzaru, 2022). In addition, TAM is

established as a simple model that captures the professionals’ behaviour

and experience of IT use and implementation (Gefen, Karahanna, &

Straub, 2003). It is built on the premise that professionals’ behaviour

and experience in IT adoption drives IT implementation and use, which

is also affected by practitioners’ perceived ease of use and usefulness.

However, it may overlook other factors that inuence user behavior,

such as individual characteristics, experiences, and motivations. TAM

assumes a static nature, implying that user perceptions remain consis-

tent over time, but attitudes and beliefs may change as individuals ac-

quire experience or the technology evolves (Muthia & Siti, 2023). It also

does not account for extrinsic variables like social characteristics or

organizational culture, limiting its explanatory ability in complex

real-world scenarios (Muthia & Siti, 2023; Tulasi, 2022). It focuses on

behavioral intention rather than actual behavior, and does not account

for reverse causation or bidirectional interactions (Bara’ah et al., 2022;

Meiryani et al., 2021). It also overemphasizes rational decision-making,

assuming consumers make rational choices based on ease of use and

utility (Malatji et al., 2020).

According to Ferri et al. (2021b), perceived ease of use and perceived

usefulness are the two motivational elements driving IT usage intention.

A.A.H. Abdullah and F.A. Almaqtari

Journal of Open Innovation: Technology, Market, and Complexity 10 (2024) 100218

Perceived utility reects a person’s expectation of how a new IT system

would boost their work performance. The second element, perceived

ease of use, is an individual’s perception of the effort involved in

adopting a new work pattern (Ferri et al., 2021a). It also relates to users’

perceptions of the benets provided by a specic technology (Grani

Maranguni

c, 2019). In this sense, the TAM model’s perceived usefulness

plays an important role in understanding user behavior and desire to use

technology (Davis, 1989). Interestingly, usefulness of a technology is

inuenced by its simplicity of use (Davis, 1989; Davis et al., 1989;

Venkatesh et al., 2003). Pedrosa et al. (2020) validated this association

between the two elements in a study on the drivers of computer-assisted

auditing tools (CAATs) utilization among statutory auditors in a Euro-

pean country.

IT perceived usefulness is also a crucial element inuencing its

acceptance in several businesses, including auditing (Pedrosa et al.,

2020). Research found that if a technology offers high benets, users are

more likely to favor it over alternative applications (Davis, 1989; Mei

and Aun, 2019; Dharma et al., 2017; Al-Okaily, 2022; Thottoli et al.,

2022). Pedrosa et al. (2020) and Dharma et al. (2017) also indicate that

perceived benet has a signicant effect on IT audit adoption, which

supports the argument of the effect of technology benets on its

acceptability. As a result, if a technology is seen to be lacking benets, it

is unlikely to be adopted (Mei and Aun, 2019), which explains why IT

audit adoption can improve the audit profession and practices (Dowling,

2009; Thottoli & Thomas, 2022; Purnamasari et al., 2022; Pedrosa et al.,

2020). On the other hand, several researchers have found that perceived

benets have a marginal effect on IT audit adoption, e.g. (Kim et al.,

2016).

Although many studies used this model to investigate technological

acceptability in accounting and auditing (Venkatesh and Bala, 2008;

arzaru, 2022; Alshurafat et al., 2021; Ferri et al., 2021a; Ferri et al.,

2021b; Usman et al., 2022; Qasim, Kharbat, 2020; Pedrosa et al., 2020),

very few explored the perceived usefulness and ease of use of AI and

Industry 4.0 in auditing and accounting in developing nations. As a

result, this study employs this model, which has been also followed by

many prior studies, to assess the acceptance and use of a new IT (e.g.,

Siew et al., 2020; Thottoli & Thomas, 2022; Pedrosa et al., 2020). To this

end, the current study investigates how the Technology Acceptance

Model (TAM) mediates the impact of AI and Industry 4.0 on accounting

and auditing practices. Based on this discussion, the hypotheses are

formulated as follows:

H1. . Perceived usefulness and ease of use mediate the relationship

between the intention to use AI and accounting and auditing practices in

Saudi Arabia.

This hypothesis can be further divided into the following sub-

hypotheses:

H1a. . Perceived usefulness mediates the relationship between the

intention to use AI and accounting and auditing practices in Saudi

Arabia.

H1b. . Perceived ease of use mediates the relationship between the

intention to use AI and accounting and auditing practices in Saudi

Arabia.

H2. . Perceived usefulness and ease of use mediate the relationship

between the intention to use Industry 4.0 and accounting and auditing

practices in Saudi Arabia.

Based on this hypothesis, following sub-hypotheses are framed:

H2a. . Perceived usefulness mediates the relationship between the

intention to use Industry 4.0 and accounting and auditing practices in

Saudi Arabia.

H2b. . Perceived ease of use mediates the relationship between the

intention to use Industry 4.0 and accounting and auditing practices in

Saudi Arabia.

The effect of AI

The role of articial intelligence (AI) in business operations has been

extensively studied and recognized for its signicant impact across

various domains (Dhamija & Bag, 2020). One such domain where AI has

shown potential is talent acquisition, and its adoption can be inuenced

by factors such as task-technology t (Dishaw & Strong, 1999). The

alignment between tasks performed and AI technology capabilities plays

a crucial role in its acceptance. Additionally, understanding the adop-

tion of AI-based technologies in different contexts is important (Dhamija

& Bag, 2020). As businesses use AI to reinvent processes and generate

evidence-based data analysis, AI governance is critical for digital inno-

vation (Papagiannidis et al., 2022). Singapore leads the area in AI

experimentation across multiple industries, whereas Malaysia is grad-

ually using AI in accordance with the TN50 and IR 4.0. Early adopters

such as Singapore, Malaysia, Vietnam, Indonesia, the Philippines, and

Thailand might obtain a competitive advantage by adopting AI as a

potential revenue source, however ASEAN countries such as Cambodia,

Myanmar, Brunei, and Laos have low adoption (Mohd Noor and Mansor,

2019). Thus, research is crucial in aligning theory and practice in

auditing, with some leading rms integrating big data into their prac-

tices (Gepp et al., 2018; Schmitz and Leoni, 2019).

AI has brought forth various benets and transformations in the

accounting profession. It can automate monotonous procedures,

simplify data analysis, make better decisions, and streamline auditing

processes (Dhamija & Bag, 2020). The adoption of technology, specif-

ically articial intelligence (AI) (Munoko et al., 2020; Han et al., 2023;

Al-Sayyed et al., 2021; Zhang et al., 2020), and Industry 4.0 (Tavares

et al., 2022; Fül

op et al., 2022;

Ozcan and Akkaya, 2020), has had a

signicant impact on the eld of accounting and auditing worldwide.

Using AI including machine learning, deep learning, big data analytics,

data mining, and cloud computing in accounting and auditing practices

has leveraged the possibility of processing massive volumes of nancial

data, facilitating the identication of patterns, trends, and anomalies.

Further, the use of AI in accounting and auditing has improved

decision-making abilities (Coman et al., 2022; Lehner et al., 2022; Yoon,

2020).

AI has revolutionized accounting and auditing by automating com-

mon operations like data entry (V

arzaru, 2022) and reconciliation

(Shaffer et al., 2020), allowing accountants to focus on more challenging

tasks (Munoko et al., 2020; V

arzaru, 2022; Zhang et al., 2020).

AI-powered automation solutions, such as robotic process automation

(RPA) systems (Gotthardt et al., 2020; Losbichler and Lehner, 2021),

extract (Sledgianowski et al., 2017), categorize (Issa et al., 2016;

O’Leary, 2009; Sledgianowski et al., 2017), and enter data (V

arzaru,

2022), enabling faster nancial forecasting and identifying anomalies in

nancial records (Chen, 2021; Faccia et al., 2019; Losbichler and Leh-

ner, 2021; Zhang et al., 2020). AI also improves decision-making by

analyzing massive amounts of data (Gomez, 2018; Kopalle et al., 2022;

Yoon, 2020) and discovering patterns in real-time (Sledgianowski et al.,

2017; Sutton et al., 2016). Continuous auditing tools, developed by AI,

allow real-time monitoring of nancial activities (Sledgianowski et al.,

2017; Sutton et al., 2016), reducing errors and enabling a more thorough

evaluation of nancial data (Faccia et al., 2019; V

arzaru, 2022). AI can

predict future trends based on historical data (Gomez, 2018; Sutton

et al., 2016), improve nancial forecasting (Chen, 2021; Losbichler and

Lehner, 2021), budgeting, and detect fraudulent transactions (Gepp

et al., 2018; Sledgianowski et al., 2017; Yoon, 2020). Natural Language

Processing (NLP) allows AI to analyze unstructured text (Munoko et al.,

2020; Sun and Vasarhelyi, 2018; Tiberius and Hirth, 2019), improve

compliance, and handle nancial inquiries (Sun and Vasarhelyi, 2018).

AI also automatically detects transactions that violate regulatory stan-

dards, reducing noncompliance risks (Gepp et al., 2018; Sledgianowski

et al., 2017). Robotic Process Automation (RPA) systems can automate

regular bookkeeping activities (Earley, 2015; Faccia et al., 2019), min-

imising the need for human involvement (Gotthardt et al., 2020;

A.A.H. Abdullah and F.A. Almaqtari

Journal of Open Innovation: Technology, Market, and Complexity 10 (2024) 100218

O’Leary, 2009). AI algorithms can also rapidly analyse big datasets

(Brown-Liburd and Vasarhelyi, 2015; Cockcroft and Russell, 2018; Sal-

ijeni et al., 2019), detecting patterns, trends, and abnormalities (Gomez,

2018; Kopalle et al., 2022; Sledgianowski et al., 2017; Sutton et al.,

2016; Yoon, 2020). This allows auditors to discover abnormalities or

patterns that may indicate fraud (Gomez, 2018; Kopalle et al., 2022;

Yoon, 2020).

AI algorithms can automate typical accounting processes like data

entry and transaction processing, saving time and decreasing human

error (Dhamija & Bag, 2020; Handoko, 2021). Besides, AI systems can

assess massive amounts of nancial data quickly and efciently, assist-

ing in the detection of patterns, anomalies, and trends that people may

miss (Dhamija & Bag, 2020; Coman et al., 2022). This enhanced data

analysis capability also aids in the detection and evaluation of fraud

(Dhamija & Bag, 2020). Better decision-making has also emerged from

the use of AI in accounting and auditing because AI can provide real-

time insights and predictive analytics, helping accountants and audi-

tors to make more informed decisions (Dhamija & Bag, 2020). It can

help with nancial forecasting and scenario analysis, producing reliable

nancial predictions (Coman et al., 2012). In addition, AI can enhance

audit efciency and effectiveness by assessing nancial statements,

identifying potential hazards, and recommending areas for additional

examination (Dhamija & Bag, 2020).

While AI technology has many advantages, it is crucial to note that it

is not intended to replace human accountants and auditors but rather to

supplement their talents (Dhamija & Bag, 2020). Thus, human judgment

and decision-making are still critical (Coman et al., 2022; Yoon, 2020).

The incorporation of AI in accounting processes necessitates the devel-

opment of new abilities and the adaptation to shifting responsibilities for

accountants and auditors (Coman et al., 2022). They need to become

procient in utilizing AI tools, interpreting AI-generated insights, and

understanding the limitations and ethical considerations associated with

AI technologies (Coman et al., 2022; Lehner et al., 2022). Thus, the

following hypothesis is developed:

H3. . The usage of AI has a positive effect on accounting and auditing

practices in Saudi Arabia.

The effect of Industry 4.0

Several studies have investigated the role of Industry 4.0 in different

contexts (Burritt and Christ, 2016; Ghobakhloo, 2020; Kamble et al.,

2018; Kiel et al., 2017; Masood and Sonntag, 2020; Müller et al., 2018;

Nascimento et al., 2019; Stock and Seliger, 2016). While some studies

investigate the readiness to adopt Industry 4.0 (Masood and Sonntag,

2020; Schumacher et al., 2016), some studies investigate the challenges,

opportunities, and benets of Industry 4.0 (Alaloul et al., 2020; Gho-

bakhloo, 2020, 2020; Ivanov et al., 2021; Kiel et al., 2017; Masood and

Sonntag, 2020; Müller et al., 2018; Stock and Seliger, 2016). Recently,

the focus on Industry 4.0 is on the enhancement of the performance by

reducing errors, increase product quality, freeing humans from menial

and/or dangerous tasks and providing consumers with the products they

desire at times when they desire them (Burritt and Christ, 2016). In the

new Industry 4.0 paradigm, Boyer, Kokosy (2022) investigated how IT

innovation tools might support collaborative governance. Boyer, Kokosy

(2022) emphasises how complex interactions among actors can spark

fresh ideas and successfully implement Industry 4.0 advancements. This

may necessitates integrating IT innovation tools and collaborative

governance, which can effectively address Industry 4.0 concerns, stim-

ulating creativity and resolving potential intergovernmental conicts

(Hwang, 2017).

China’s Industry 4.0 development is heavily reliant on the integra-

tion of Industry 4.0 and improved articial intelligence performance.

Furthermore, the development of China’s Industry 4.0 is dependent on

delivering a high level of articial intelligence, the requisite nancial

resources, the formation of high level and advanced industrial zones, the

enhancement of production processes, and research collaborations. As a

result, advanced Industry 4.0 with smart production is achieved (Hou

et al., 2020). While there is a growing body of literature on AI’s po-

tential, there is a lack of comprehensive studies covering all Industry 4.0

technologies, including the Internet of Things, big data analytics, and

cloud computing. A comprehensive study linking these technologies to

AI in accounting and auditing could help understand the synergies and

challenges of these technologies in the nancial sector. According, the

current study hypothesize that:

H4. . Industry 4.0 readiness has a positive effect on accounting and

auditing practices in Saudi Arabia.

Research design

Conceptual framework and research design

The research design outlined in Fig. 1 focuses on examining the in-

uence of AI on accounting and auditing practices. The independent

variables are AI (big data, deep learning, and cloud computing), In-

dustry 4.0 readiness, and the Technology Acceptance Model (TAM)

variables (ease of use, perceived usefulness, and intention to use AI). The

dependent variables pertain to various aspects of accounting and

auditing practices, including accounting education, auditing practices

(audit planning, audit process, and audit reporting), and accounting

practices (strategic planning and budgeting, reporting and taxation, and

costing). The study intends to investigate how AI, Industry 4.0 readiness,

and TAM components inuence accounting and auditing practices by

adding these variables into the research methodology.

The research model presents a mediation and path framework to

evaluate the interactions between these factors. It implies that AI and

Industry 4.0 readiness operate as predictors, inuencing the intention to

use AI in accounting and auditing practices. The aim to adopt IT, as well

as the anticipated benets, operate as meditators that inuence the nal

output, which is accounting and auditing practices.

The inclusion of accounting and auditing practices as dependent

variables allows a more in-depth evaluation of the inuence of IT on

different parts of these practices. Investigating how AI and Industry 4.0

readiness affect accounting education, for example, can provide insights

into the evolving skill sets required of accountants in the digital era.

Similarly, exploring the impact of AI and Industry 4.0 readiness on

auditing practices, such as audit planning, process, and reporting, can

shed light on the efciency and effectiveness improvements brought

about by the two technologies. Lastly, examining AI and Industry 4.0

readiness inuence on accounting practices, including strategic plan-

ning and budgeting, reporting and taxation, and costing, can offer in-

sights into how technology adoption affects nancial management and

decision-making processes.

Data and sample

The population of the study consists of respondents from Saudi

Arabia, which aligns with the research objective of examining the

impact of AI and Industry 4.0 readiness on accounting and auditing

practices in this context. The study employs a non-probability conve-

nience sampling approach, as well as snowball sampling, which is

justied by previous research suggesting their suitability for processing

multivariate data and estimating results. The study used literature and

previous research to estimate the sample size, based on assumed effect

sizes relevant to the investigation. The researchers used established

methods and standards from prior research studies by Bollen (1989),

Christopher Westland (2010), and Long et al. (1990) to determine the

minimal sample size required for the study. They calculated the sample

size using free statistical software based on PLS path modelling, ac-

counting for both latent and observable variables, effect magnitude,

statistical power level, and probability level. The calculated sample size

A.A.H. Abdullah and F.A. Almaqtari

Journal of Open Innovation: Technology, Market, and Complexity 10 (2024) 100218

yielded 148 participants. Other inputs included the desired level of

condence and statistical power, with a goal of achieving a condence

level of 95% to ensure the accuracy of the ndings. In this regard, G-

Power software was used, which suggested a sample size of 163 re-

spondents. The actual data collection method, however, resulted in the

collection of 228 surveys using an online questionnaire sent via Google

Docs. To disseminate the survey and collect data, the researchers used

several social media platforms such as Facebook, WhatsApp, and email.

To enhance response rates and reduce low-quality responses, measures

such as making all questions mandatory and utilizing respondent-

friendly phrasing for closed-ended questions were implemented to

assure thorough data. The researchers also used targeted distribution

platforms with brief letters that emphasized brevity, which resulted in a

20% boost in response rate.

Finally, the study’s nal sample size was determined to be 228 sur-

vey respondents, which were regarded statistically sufcient for pre-

dicting the outcomes. This conclusion is supported by the sampling and

sample adequacy analyses presented in Table 1. The Kaiser-Meyer-Olkin

Sampling Adequacy Measure returned a value greater than 0.7, which is

considered satisfactory. Furthermore, the signicance level for this test

was quite high at 1%, indicating that the sample t and was appropriate.

With a value of 8927.44 and 789 degrees of freedom, Bartlett’s test also

conrmed the sufciency of the factor analysis.

Research instrument

The current study used an online questionnaire survey to collect data

from a variety of respondents, including external auditors, board

members, CFOs, senior executives, internal auditors, and accountants

from various Saudi organizations. The research instrument was devel-

oped based on relevant literature to ensure that it addressed all major

topics related to the research objectives. The research instrument in-

cludes 58 items that were carefully constructed to represent each spe-

cic dimension of the current study. The items were developed to

perceive respondents’ perceptions on the impact of AI and Industry 4.0

Fig. 1. Research Framework.

Table 1

Sampling adequacy Test.

Particulars Total

Total number of completed surveys (Online) 228

The number of incomplete surveys (0)

Total number of questionnaire forms processed 228

KMO and Bartlett’s Test

“Kaiser-Meyer-Olkin Measure of Sampling Adequacy”. 0.927

“Bartlett’s Test of Sphericity”

Approx. Chi-Square 8927.44

Degree of freedom 789

Sig. 0.000

A.A.H. Abdullah and F.A. Almaqtari

Journal of Open Innovation: Technology, Market, and Complexity 10 (2024) 100218

readiness on accounting and auditing processes in Saudi Arabia. All

items included in the survey were constructed using a 5-point Likert

scale. The scale ranges from 1 to 5. While 1 indicates "strongly disagree",

the scale of 5 represents "strongly agree." This enabled the researchers to

investigate how much respondents agreed or disagreed with the state-

ments of the survey. To ensure comprehensiveness, the questionnaire

was structured into fourteen dimensions. These dimensions encom-

passed various aspects relevant to the research objectives, covering

topics such as big data, deep learning, cloud computing, Industry 4.0

readiness, ease of use, perceived usefulness, intention to use AI, ac-

counting education, audit planning, audit process, audit reporting, ac-

counting strategic planning and budgeting, reporting and taxation, and

costing. Table 2 provides the measurement scales synthesized from prior

studies and Appendix I demonstrates the operational denition of the

variables.

Empirical results

Demographic analysis of the sample

Table 3 provides an overview of the respondents’ prole in terms of

their work experience and current positions. The ndings indicate that

among the participants, 27% had 5 years or less of work experience,

while 42% had 6 to 10 years of experience.

A total of 22% had 11 to 15 years of experience, and 9% had more

than 15 years of experience. The majority of respondents (42%) worked

as CFOs, internal auditors, or accountants in their current roles. CPAs

made up 25% of the sample, board members made up 18%, and aca-

demics made up 15%.

Model’s measurement

Factor loadings

Table 4 shows the results of the conrmatory factor analysis using

Partial Least Squares (PLS). The ndings reveal that the items used to

measure each construct have high relationships with the structures they

are measuring. The value of each item’s factor loadings exceeds the

criterion threshold of 0.50, as proposed by Chin (2010). This shows a

high level of convergent validity. The ndings of the factor loadings

show that the values range from 0.515 to 0.992, indicating a strong link

between the items and their underlying constructs. The item factor

loading values above the recommended threshold value (0.50) was

suggested by Chin et al. (2008). For example, item AIINT2 exhibits a

factor loading of 0.958, implying that it has a signicant link with the

construct "AI." Similarly, items related to "Ease of Use" (EASE1, EASE2,

EASE3) and "Strategic Planning & Budgeting" (STRPLN1, STRPLN2,

STRPLN3) had factor loadings exceeding 0.75 suggest a strong shared

variance with the latent constructs.

The factor loadings provide evidence for the convergent validity of

the measurement model used in the study because they indicate that the

items effectively measure their respective constructs and contribute

signicantly to the overall measurement model. Consequently, these

ndings enhance the reliability and validity of the study’s measurement

instrument, as well. Fig. 2 also demonstrates the conrmatory factor

analysis.

Validity and reliability

Table 5 displays the reliability and validity measures for each

construct in the study. The ndings show the indicators for Cronbach’s

alpha, Rho_A, composite reliability, and average variance extracted

(AVE). Cronbach’s alpha measures internal consistency reliability,

which indicates how closely the items within a construct are related to

each other. In this study, all constructs have Cronbach’s alpha values

above 0.70, ranging from 0.770 to 0.945. These values suggest good

internal consistency, indicating that the items within each construct are

reliably measuring the same underlying concept. Furthermore, Rho_A,

another measure of reliability, displays high values for all constructs,

ranging from 0.771 to 0.946. These values add to the measurement

items’ internal consistency.

Composite reliability assesses the construct’s dependability by

considering both the items’ internal consistency and their in-

tercorrelations. Like Cronbach’s alpha and Rho_A, composite reliability

values greater than 0.70 imply high dependability. Since all constructs

in this investigation have composite reliability values ranging from

0.748 to 0.945, there is strong dependability. Finally, the average

variance extracted (AVE) evaluates the amount of variance captured by

the construct in comparison to the measurement error. AVE values

greater than 0.50 are generally regarded as satisfactory, suggesting that

the measuring items explain more than half of the variance in the

construct. All constructs in this investigation have AVE values greater

than 0.50, ranging from 0.517 to 0.790, which shows that the constructs

properly capture the underlying variance. Overall, the constructs in the

study had high reliability and validity measures. Cronbach’s alpha,

Rho_A, composite reliability, and AVE are all in the acceptable range,

indicating good internal consistency, reliability, and convergent val-

idity, which implies that the measuring items in the study exhibit reli-

ability and validity in assessing their respective constructs.

Discriminant validity

Table 6 shows the results of the discriminant validity analysis. We

can observe that the diagonal elements are greater than the correlations

between constructs for most cases, indicating good discriminant val-

idity. The ndings demonstrate strong correlation values among items

Table 2

Operational denition of the variables.

Construct Evidence

AI Big Data Mikalef and Gupta (2021)

Deepl Learning Sun and Vasarhelyi (2018) and

Issa et al. (2016)

Cloud Computing Mikalef and Gupta (2021)

Industry 4.0 Industry 4.0 readiness Müller et al. (2018), Elazhary

et al. (2022),and Amoozad

Mahdiraji et al.(2022)

TAM Perceived ease of use Damerji and Salimi (2021) and

Davis (1989)

Percievd usefulness Damerji and Salimi (2021) Razi

and Madani (2013)

Intention to Use Venkatesh et al. (2012), Cao

et al. (2021), and Razi and

Madani (2013)

Accounting

education

Accounting education Xu and Babaian (2021),Guan

et al. (2020), andZhang et al.

(2020)

Audting

practices

Audit planning, process, and

reporting

Issa et al. (2016)

Accounting

practices

Costing, reporting and

taxation, and strategic

planing and budgeting

Aqlan (2021)

Table 3

Respondents’ prole.

Work Experience

Categories Freq. %

5 Years and less 62 27%

6:10 95 42%

11:15 50 22%

More than 15 21 9%

Total 228 100%

Current Position

CPA 57 25%

Board Member 41 18%

CFO/ Internal Auditor/ Accountant 96 42%

Academician 34 15%

Total 228 100%

A.A.H. Abdullah and F.A. Almaqtari

Journal of Open Innovation: Technology, Market, and Complexity 10 (2024) 100218

that assess the same construct, indicating that these items accurately

represent their respective construct and not any other construct. This

observation is supported by the fact that the correlation values between

each construct and other constructs are lower than the self-correlation

values of the construct itself (Fornell and Larcker, 1981).

Descriptive statistics

Table 7 provides descriptive statistics for the variables of the study.

The results show that the range of the variables is 4, which is between a

minimum value of 1 and a maximum value of 5 except for IR4.0, AIINT,

AUDPR, AUDREP, REP&TAX, STRPLN that have a range value of 3 (Min.

= 2 and Max. = 5). Further, the results indicate that the average values

of the variables are about 4, indicating that the respondents perceived a

positive perception of the statements asked. The results also show that

the skewness and the kurtosis values are in the range of ( ± 1) for

skewness and ( ± 3) for kurtosis, which indicate that the data is nor-

mally distributed.

Structural model

Fig. 3 displays the hypothesized or predicted structural approach for

the current study variables. It provides the direct effect model as pre-

sented in Fig. 1.

Results estimation- direct effect

Table 8 provides the estimates of SEM analysis. The ndings suggest

a potential positive association between AI and perceived ease of use. A

moderately strong positive coefcient (β + = 0.408) with a high level of

signicance (p = 0.001) reveals a possible inuence of AI on Ease of

Use. This tentative evidence suggests that AI technologies may be

perceived as user-friendly and relatively simple to use in the context of

accounting and auditing practices. Additionally, the study indicates a

potential link between AI and the perceived usefulness of technology. A

positive coefcient (β + = 0.349) and a highly signicant connection

(p = 0.005) tentatively support the idea that AI could be considered

advantageous and valuable for enhancing accounting and auditing tasks

in Saudi Arabia. This is consistent with several studies (e.g., Munoko

Table 4

Factor Loadings.

Items 1 2 3 4 5 6 7 8 9 10 11 12 13 14

BIGD1 0.868

BIGD2 0.806

BIGD3 0.734

CLOD1 0.718

CLOD2 0.643

CLOD3 0.752

DEEPL1 0.864

DEEPL2 0.74

DEEPL3 0.876

IR4.0_1 0.66

IR4.0_2 0.866

IR4.0_3 0.886

IR4.0_4 0.908

EASE1 0.846

EASE2 0.786

EASE3 0.788

USEFUL1 0.858

USEFUL2 0.834

USEFUL3 0.827

USEFUL4 0.787

USEFUL5 0.749

USEFUL6 0.719

AIINT1 0.765

AIINT2 0.958

ITEDU1 0.751

ITEDU2 0.924

ITEDU3 0.794

ITEDU4 0.691

ITEDU5 0.992

AUDPLN1 0.691

AUDPLN2 0.809

AUDPLN3 0.817

AUDPR1 0.904

AUDPR2 0.77

AUDPR3 0.556

AUDPR4 0.827

AUDREP1 0.784

AUDREP2 0.799

STRPLN1 0.867

STRPLN2 0.85

STRPLN3 0.819

REP&TAX1 0.836

REP&TAX2 0.844

REP&TAX3 0.836

PLN&COS1 0.906

PLN&COS2 0.883

PLN&COS3 0.877

(1) Big Data, (2) Cloud _Computing, (3) Deep _Learning, (4) Industry 4.0 readiness, (5) Ease of Use, (6) Perceived _Usefulness, (7) Intention to Use AI, (8)Accounting

Education, (9) Audit Planning, (10) Audit Process, (11) Audit _Reporting, (12) Strategic _Planning &_Budgeting, (13) Reporting &_Taxation, (14) Costing

A.A.H. Abdullah and F.A. Almaqtari

Journal of Open Innovation: Technology, Market, and Complexity 10 (2024) 100218

et al., 2020; Han et al., 2023; Al-Sayyed et al., 2021; Zhang et al., 2020)

that indicated that accounting and auditing practices are signicantly

affected by AI in different countries. Consistently, Correia et al. (2020)

and Thottoli et al. (2022) indicated that the application of AI has become

important to the efcacy and efciency of accounting and auditing

activities.

The results of the present study demonstrate that AI dimensions such

as Big Data, Cloud Computing, and Deep Learning associates positively

with AI acceptance and utilization. The effect of Big Data on AI

acceptability is especially signicant, with apositive coefcient (β + =

0.538) and high signicance (p = 0.001). This robust relationship un-

derscores the pivotal role of Big Data in fostering the adoption of AI in

accounting and auditing practices. In the same context, a positive cor-

relation (β + = 0.460) and a signicant association (p = 0.001) support

the inuence of Cloud Computing on AI. This suggests that using cloud-

based technology may be perceived as facilitating easier AI integration

and implementation in the accounting and auditing arena. In addition,

the results demonstrated a signicant relation between Deep Learning

and AI, evidenced by a positive coefcient (β + = 0.438) and a high

level of signicance (p = 0.001). This underscores the signicance of

advanced machine learning methodologies, such as Deep Learning, in

enhancing the capabilities of AI for applications in accounting and

auditing.

The study ndings also emphasize the importance of perceived ease

of use in affecting the intention to use AI. A positive coefcient (β + =

0.239) and a statistical signicance (p = 0.009) support the inuence of

Ease of Use on Intention to Use AI, indicating that respondents who

believe AI is simple to use are more likely to have a strong desire to

Fig. 2. Conrmatory Factor Analysis.

Table 5

Validity and reliability for constructs.

Variables Cronbach’s

Alpha

rho_A Composite

Reliability

Average

Variance

Extracted (AVE)

AI 0.832 0.840 0.833 0.50

Accounting

Education

0.923 0.934 0.921 0.702

Accounting

Practices

0.945 0.946 0.945 0.657

Audit Reporting 0.770 0.771 0.770 0.627

Audit Planning 0.812 0.823 0.817 0.600

Audit Process 0.858 0.875 0.854 0.601

Auditing Practices 0.905 0.907 0.906 0.517

Big Data 0.844 0.851 0.846 0.647

Cloud _Computing 0.751 0.752 0.748 0.51

Costing 0.919 0.919 0.919 0.790

Deep _Learning 0.866 0.874 0.868 0.687

Ease of Use 0.849 0.850 0.848 0.651

Industry 4.0

readiness

0.897 0.913 0.902 0.699

Intention to Use

0.846 0.876 0.857 0.751

Perceived

_Usefulness

0.913 0.915 0.913 0.636

Reporting

&_Taxation

0.877 0.877 0.877 0.703

Strategic

_Planning

&_Budgeting

0.882 0.883 0.883 0.715

A.A.H. Abdullah and F.A. Almaqtari

Journal of Open Innovation: Technology, Market, and Complexity 10 (2024) 100218

adopt and apply AI technology in their professional operations. The

study does, however, indicate a weak positive association between

perceived ease of use and perceived usefulness of AI, as evidenced by a

positive coefcient (β + = 0.199). However, the relationship is not

statistically signicant (p = 0.254).

The ndings also show that Industry 4.0 readiness has a signicant

impact on AI’s perceived ease of use and usefulness. A moderately strong

positive coefcient (β + = 0.511) and statistical signicance

(p = 0.001) support the effect of Industry 4.0 readiness on Ease of Use.

Similarly, a positive coefcient (β + = 0.302) and statistical signicance

(p = 0.014) support the effect of Industry 4.0 readiness on perceived

usefulness. This demonstrates the importance of Industry 4.0 readiness

in impacting the usability and perceived value of AI in accounting and

auditing operations.

The study found a link between the intention to use AI and ac-

counting education, accounting processes, and auditing methods. A

positive association (β + = 0.399) and signicant effect (p = 0.001)

support the effect of Intention to Use AI on Accounting Education.

Similarly, large positive coefcients (β + = 0.631 and β + = 0.629,

respectively) and high signicance (p = 0.001) support the effect of

Intention to Use AI on Accounting and Auditing Practices.

Results estimation- indirect effect

The effect of AI

The results in Table 9 indicate a positive coefcient (β + = 0.132)

and a signicant relationship (p = 0.001) of the integration of AI in

accounting education. It has been also found that AI positively in-

uences accounting processes (AI -> Accounting procedures) with a

coefcient (β + = 0.208) and a signicant association (p = 0.001). This

highlights AI’s ability to improve accounting systems by automating

tasks, increasing data accuracy, and giving advanced analytical

capabilities.

In addition, the results indicate that AI has a signicant effect on

audit reporting with (β + = 0.194) and a signicant (p = 0.001), which

support that AI can assist auditors in analyzing massive volumes of

nancial data, detecting anomalies, and improving audit report quality.

Furthermore, AI has a signicant impact on audit planning with (β + =

0.211) and a signicant effect (p = 0.001), which means that by

assessing prior data and offering applicable audit procedures, AI tech-

nology can help to enhance resource allocation and efciency.

The present study also identied a positive relationship (p = 0.001)

between AI and the total audit process (AI -> Audit Process) (β + =

0.218). This indicates that AI may automate certain audit operations,

reducing manual errors and freeing up auditors to focus on higher-value

Table 6

Discriminant validity.

Var 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

1 0.60

2 0.25 0.84

3 0.50 0.32 0.81

4 0.59 0.33 0.81 0.79

5 0.60 0.38 0.72 0.68 0.77

6 0.53 0.26 0.79 0.78 0.86 0.78

7 0.61 0.35 0.84 0.94 1.02 1.05 0.72

8 0.98 0.25 0.46 0.46 0.53 0.43 0.51 0.81

9 0.90 -0.13 0.30 0.43 0.36 0.40 0.42 0.50 0.71

10 0.49 0.26 0.99 0.78 0.69 0.76 0.80 0.47 0.32 0.89

11 0.87 0.40 0.37 0.48 0.47 0.40 0.48 0.47 0.38 0.32 0.83

12 0.73 0.07 0.55 0.39 0.49 0.57 0.55 0.67 0.65 0.51 0.36 0.81

13 0.63 0.01 0.54 0.37 0.46 0.51 0.50 0.39 0.56 0.51 0.53 0.77 0.84

14 0.67 0.40 0.63 0.54 0.62 0.58 0.63 0.52 0.37 0.63 0.64 0.61 0.62 0.87

15 0.68 0.29 0.66 0.60 0.69 0.76 0.76 0.56 0.52 0.64 0.50 0.69 0.67 0.70 0.80

16 0.42 0.35 1.02 0.83 0.65 0.72 0.78 0.33 0.23 0.87 0.40 0.43 0.48 0.57 0.58 0.84

17 0.50 0.30 1.00 0.70 0.71 0.77 0.79 0.50 0.29 0.83 0.32 0.60 0.53 0.59 0.66 0.88 0.85

Note: (1) AI, (2) Accounting Education, (3) Accounting Practices, (4) Audit Reporting, (5) Audit Planning, (6) Audit Process, (7) Auditing _Practices, (8) Big Data, (9)

Cloud _Computing, (10) Costing, (11) Deep Learning, (12) Ease of Use, (13) Industry 4.0 readiness, (14) Intention to Use AI, (15) Perceived _Usefulness, (16), Reporting

&_Taxation, (17) Strategic _Planning &_Budgeting

Table 7

Descriptive statistics.

Variables Min. Max. Range Mean St. Error Median Mode St. Dev. Kurtosis Skewness

AI 1 5 4 3.81 0.05 4 4 0.77 1.2 -0.76

BIGD 1 5 4 3.59 0.05 3.83 4 0.74 1.22 -0.78

CLOD 2 5 3 3.78 0.04 4 4 0.67 0.2 -0.5

DEEPL 1 5 4 3.48 0.05 3.67 4 0.77 0.19 -0.31

IR4.0 2 5 3 3.95 0.05 4 4 0.7 0.49 -0.7

EASE 1 5 4 3.84 0.05 4 4 0.77 1.74 -1.07

USEFUL 1 5 4 3.94 0.05 4 4 0.73 1.42 -0.88

AIINT 2 5 4 3.47 0.05 3.58 4 0.72 0.06 -0.62

ITEDU 1 5 4 3.34 0.06 3.4 4 0.85 0.25 -0.71

AUDPLN 1 5 4 3.6 0.05 3.67 4 0.69 1.47 -0.78

AUDPR 2 5 4 3.61 0.05 3.75 4 0.71 0 -0.33

AUDREP 2 5 4 3.61 0.05 4 4 0.7 0.34 -0.47

REP&TAX 2 5 3 3.7 0.04 4 4 0.68 -0.03 -0.1

PLN&COS 1 5 4 3.8 0.05 4 4 0.73 0.46 -0.48

STRPLN 2 5 3 3.65 0.05 4 4 0.76 -0.2 -0.34

Notes: AI is articial Intelligence, BIGD is Big Data, CLOD is Cloud Computing, DEEPL is Deep Learning, IR4.0 is Industry 4.0 readiness, EASE is Ease of Use, USEFUL is

Perceived Usefulness, AIINT is Intention to Use AI, ITEDU is Accounting Education, AUDPLN Audit Planning, AUDPR is Audit Process, AUDREP is Audit Reporting,

REP&TAX is Reporting &_Taxation, PLN&COS is Costing, and STRPLN is Strategic _Planning &_Budgeting.

A.A.H. Abdullah and F.A. Almaqtari

Journal of Open Innovation: Technology, Market, and Complexity 10 (2024) 100218

duties, which increases efciency and effectiveness.

In terms of general auditing procedures (AI -> Auditing procedures),

AI has a positive coefcient (β + = 0.208) and a signicant association

(p = 0.001). This could mean that the integration of AI technologies can

improve auditing procedures by automating data collection and anal-

ysis, increasing risk assessment, and providing valuable insights. In the

same context, AI has a positive signicant impact on costing (AI ->

Costing), (β + = 0.205; p = 0.001 < 0.01). Organizations may improve

cost management and decision-making by employing AI to optimize cost

accounting processes, analyze production data, and identify cost drivers.

Furthermore, exposure to AI has a positive and signicant association on

the intention to use AI (AI -> Intention to Use AI), (β + = 0.330;

p = 0.001). Respondents perceive AI benets like increased efciency

and accuracy, which leads to a stronger intention to implement AI into

their professional operations.

Although the association between AI and perceived usefulness (AI ->

Perceived Usefulness) is weak (β = 0.081), it is not statistically signi-

cant (p = 0.285). This shows that the evidence does not support the

impact of AI on respondent’s perceptions of usefulness in accounting and

nancial practices. Finally, AI has a signicant positive relationship

with reporting and taxes (AI -> Reporting and taxes) (β + = 0.211). This

Fig. 3. Structural Equation Model- Direct Effect.

Table 8

SEM Estimation – Direct Effect Model.

Path β Standard

Deviation

(STDEV)

T Statistics (|

O/STDEV|)

Values

AI -> Ease of Use 0.408 0.078 5.217 0.000

AI -> Perceived

_Usefulness

0.349 0.123 2.842 0.005

Big Data -> AI 0.538 0.036 15.079 0.000

Cloud _Computing -> AI 0.460 0.038 11.971 0.000

Deep _Learning -> AI 0.438 0.040 10.921 0.000

Ease of Use -> Intention

to Use AI

0.239 0.091 2.636 0.009

Ease of Use -> Perceived

_Usefulness

0.199 0.174 1.141 0.254

Industry 4.0 readiness

-> Ease of Use

0.511 0.078 6.545 0.000

Industry 4.0 readiness

-> Perceived

_Usefulness

0.302 0.122 2.468 0.014

Intention to Use AI

-> Accounting

Education

0.399 0.065 6.141 0.000

Intention to Use AI

-> Accounting

Practices

0.631 0.066 9.596 0.000

Intention to Use AI

-> Auditing Practices

0.629 0.064 9.775 0.000

Perceived _Usefulness

-> Intention to Use AI

0.541 0.107 5.076 0.000

Table 9

SEM Estimation – Indirect Effect Model - AI.

Path β STDEV T Stat P Values

AI -> Accounting Education 0.132 0.032 4.119 0.000

AI -> Accounting Practices 0.208 0.050 4.147 0.000

AI -> Audit Reporting 0.194 0.050 3.908 0.000

AI -> Audit Planning 0.211 0.053 3.988 0.000

AI -> Audit Process 0.218 0.054 4.072 0.000

AI -> Auditing Practices 0.208 0.051 4.060 0.000

AI -> Costing 0.205 0.050 4.111 0.000

AI -> Intention to Use AI 0.330 0.064 5.124 0.000

AI -> Perceived _Usefulness 0.081 0.076 1.070 0.285

AI -> Reporting &_Taxation 0.211 0.052 4.090 0.000

AI -> Strategic _Planning &_Budgeting 0.208 0.050 4.140 0.000

A.A.H. Abdullah and F.A. Almaqtari

Journal of Open Innovation: Technology, Market, and Complexity 10 (2024) 100218

implies AI can automate data collecting, processing, and reporting

procedures, resulting in enhanced nancial reporting and tax compli-

ance accuracy and timeliness.

The results in Table 10 exhibit that Big Data, Cloud Computing, and

Deep Learning have a statistically signicant positive inuence on ac-

counting practices and TAM model dimensions. This indicates that using

Big Data technologies in accounting education can help students learn

more effectively. Also, by embracing Big Data, educational institutions

can give students hands-on experience evaluating massive datasets,

producing data-driven insights, and comprehending its use in account-

ing. This is in line with other studies arguing the relevance of incorpo-

rating information systems and technology into the accounting

curriculum (e.g., Behn et al., 2012; Lawson et al., 2014; Apostolou et al.,

2014). Similarly, Sledgianowski et al. (2017) argue that faculty mem-

bers can actively involve accounting and non-accounting students in

accounting learning by leveraging technology and Big Data.

By analyzing cost-related data, optimizing cost allocation, and

improving cost forecasting, Deep Learning enhances organizations’

costing practices and decision-making regarding resource allocation. By

developing user-friendly interfaces, intelligent automation, and

enhanced data visualization, Deep Learning improves the user experi-

ence, simplies complex tasks, and reduces barriers to adoption.

The intention to use AI in accounting is positively inuenced by Deep

Learning, learning serves as a fundamental component of AI

technologies, inuencing individuals’ perception and acceptance of AI

in accounting practices. Individuals perceive Deep Learning as useful in

accounting. Deep Learning techniques enhance data analysis, decision-

making capabilities, and generate valuable insights, thus improving

the perceived usefulness of accounting practices. By automating data

collection, analysis, and reporting processes, Deep Learning improves

the accuracy, timeliness, and compliance of reporting and taxation

practices. By providing advanced analytics, forecasting capabilities, and

scenario analysis, Deep Learning enhances organizations’ strategic

planning and budgeting processes, enabling data-driven decisions, ac-

curate forecasts, and optimized plans and budgets.

The effect of Industry 4.0

The results in Table 11 reveal that Industry 4.0 readiness have a

signicant positive impact on various aspects of accounting. In terms of

Accounting Education, Industry 4.0 readiness shows a positive rela-

tionship. This means that incorporating concepts and tools related to

automation, data analytics, and articial intelligence into accounting

curricula, students can develop skills to adapt to technological ad-

vancements in the eld. Likewise, Industry 4.0 readiness has a positive

effect on Accounting Practices, as indicated by a coefcient of 0.215.

This indicates that Industry 4.0 readiness, such as automation, data

integration, and advanced analytics, improves the efciency and effec-

tiveness of accounting processes, reducing manual errors and enhancing

decision-making capabilities.

Additionally, a positive association was found between Audit

Reporting and Industry 4.0 readiness, with a coefcient of 0.200,

implying that automation, data analysis, and visualization tools pro-

vided by Industry 4.0 technologies enhance the accuracy and efciency

of audit reporting processes, enabling auditors to generate comprehen-

sive and real-time reports. This means that risk assessment, and resource

allocation can all be optimized using automation, leading to more ac-

curate and efcient audit plans. Likewise, Industry 4.0 readiness has a

signicant (p = 0.000) inuence on the Audit Process, suggesting that

robotic process automation and advanced data analytics improve the

efciency and efcacy of audit operations, allowing auditors to com-

plete their responsibilities more quickly and discover potential risks and

difculties. Thus, auditors can use automation, data analytics, and

articial intelligence to increase the accuracy, efciency, and effec-

tiveness of auditing operations by acquiring deeper insights, nding

patterns, and making educated judgments.

Industry 4.0 readiness is also positively associated with costing, with

a coefcient of 0.212. Its technologies can improve cost estimates, cost

allocation, and cost management through automation, real-time data

integration, and advanced analytics. Similarly, Industry 4.0

Table 10

SEM Estimation – Indirect Effect Model – AI Dimensions.

Path β STDEV T Stat P

Values

Big Data -> Accounting Education 0.071 0.018 3.881 0.000

Big Data -> Accounting Practices 0.112 0.027 4.084 0.000

Big Data -> Audit Reporting 0.105 0.027 3.838 0.000

Big Data -> Audit Planning 0.114 0.029 3.876 0.000

Big Data -> Audit Process 0.118 0.030 3.971 0.000

Big Data -> Auditing Practices 0.112 0.028 3.957 0.000

Big Data -> Costing 0.111 0.027 4.045 0.000

Big Data -> Ease of Use 0.219 0.046 4.729 0.000

Big Data -> Intention to Use AI 0.178 0.036 4.931 0.000

Big Data -> Perceived _Usefulness 0.232 0.051 4.557 0.000

Big Data -> Reporting &_Taxation 0.114 0.028 4.036 0.000

Big Data -> Strategic _Planning

&_Budgeting

0.112 0.027 4.084 0.000

Cloud _Computing -> Accounting

Education

0.061 0.016 3.757 0.000

Cloud _Computing -> Accounting

Practices

0.096 0.027 3.609 0.000

Cloud _Computing -> Audit Reporting 0.089 0.026 3.467 0.001

Cloud _Computing -> Audit Planning 0.097 0.027 3.538 0.000

Cloud _Computing -> Audit Process 0.100 0.028 3.608 0.000

Cloud _Computing -> Auditing Practices 0.096 0.027 3.602 0.000

Cloud _Computing -> Costing 0.095 0.026 3.578 0.000

Cloud _Computing -> Ease of Use 0.188 0.041 4.538 0.000

Cloud _Computing -> Intention to Use AI 0.152 0.034 4.495 0.000

Cloud _Computing -> Perceived

_Usefulness

0.198 0.047 4.228 0.000

Cloud _Computing -> Reporting

&_Taxation

0.097 0.027 3.572 0.000

Cloud _Computing -> Strategic _Planning

&_Budgeting

0.096 0.026 3.611 0.000

Deep _Learning -> Accounting Education 0.058 0.014 4.067 0.000

Deep _Learning -> Accounting Practices 0.091 0.021 4.272 0.000

Deep _Learning -> Audit Reporting 0.085 0.021 4.110 0.000

Deep _Learning -> Audit Planning 0.092 0.022 4.144 0.000

Deep _Learning -> Audit Process 0.096 0.023 4.199 0.000

Deep _Learning -> Auditing Practices 0.091 0.022 4.213 0.000

Deep _Learning -> Costing 0.090 0.021 4.263 0.000

Deep _Learning -> Ease of Use 0.178 0.033 5.448 0.000

Deep _Learning -> Intention to Use AI 0.145 0.028 5.081 0.000

Deep _Learning -> Perceived _Usefulness 0.188 0.041 4.552 0.000

Deep _Learning -> Reporting &_Taxation 0.093 0.022 4.210 0.000

Deep _Learning -> Strategic _Planning

&_Budgeting

0.091 0.021 4.270 0.000

Table 11

SEM Estimation – Indirect Effect Model – Industry 4.0 readiness.

Path β STDEV T Stat P

Values

Industry 4.0 readiness -> Accounting

Education

0.136 0.030 4.497 0.000

Industry 4.0 readiness -> Accounting

Practices

0.215 0.045 4.767 0.000

Industry 4.0 readiness -> Audit Reporting 0.200 0.041 4.866 0.000

Industry 4.0 readiness -> Audit Planning 0.218 0.043 5.015 0.000

Industry 4.0 readiness -> Audit Process 0.225 0.045 4.976 0.000

Industry 4.0 readiness -> Auditing

Practices

0.214 0.043 4.951 0.000

Industry 4.0 readiness -> Costing 0.212 0.044 4.774 0.000

Industry 4.0 readiness -> Intention to Use

0.340 0.062 5.474 0.000

Industry 4.0 readiness -> Perceived

_Usefulness

0.102 0.093 1.093 0.275

Industry 4.0 readiness -> Reporting

&_Taxation

0.218 0.046 4.723 0.000

Industry 4.0 readiness -> Strategic

_Planning &_Budgeting

0.214 0.045 4.729 0.000

A.A.H. Abdullah and F.A. Almaqtari

Journal of Open Innovation: Technology, Market, and Complexity 10 (2024) 100218

technologies foster an atmosphere conducive to the adoption and use of

articial intelligence in accounting activities, boosting decision-making,

automating procedures, and increasing overall efciency. Further,

automation, data integration, and sophisticated analytics improve

reporting and taxation procedures’ accuracy, timeliness, and efciency,

hence enhancing overall performance. Eventually, organizations with a

higher ambition to use AI are more likely to apply the technology in

accounting tasks, such as audit reporting, audit planning, audit pro-

cesses, costing, reporting and taxation, and strategic planning and

budgeting. Firms can use AI tools to automate operations, analyze data

more effectively, improve accuracy, and improve decision-making ca-

pabilities in various areas (Burritt and Christ, 2016; Fern

andez-Caram

et al., 2019; Ghobakhloo, 2018; Nagy et al., 2018).

The effect of TAM dimensions

The results in Table 12 show that ease of use, perceived usefulness,

and the intention to use AI and Industry 4.0 readiness have a positive

inuence on Accounting Education, and auditing and accounting prac-

tices. The three dimensions exhibit a statistically signicant positive

effect on audit reporting, planning, and processing. They also reveal a

statistically signicant positive effect on costing, reporting and taxation,

and strategic planning and reporting. Thus, using user-friendly AI

technologies in accounting and audit procedures increases productivity

and accuracy as these technologies make activities more accessible and

less complex for professionals. For example, using AI in audit reporting

can streamline the report-generation process, making it easier for au-

ditors to gather and effectively communicate their ndings. Further-

more, these solutions streamline the preparation of audit plans, resource

allocation, and audit procedure execution, restructuring the planning

and execution stages. User-friendly AI solutions can improve the gath-

ering, analysis, and administration of cost-related data in accounting

procedures, enhancing accuracy and efciency. Finally, by providing

intuitive interfaces and functionalities, user-friendly AI tools and soft-

ware help the development of strategic plans and budgets.

Additional analysis

Subgroup analysis is conducted to assess if there are signicant dif-

ferences in the results attributed to the demographic variables. The re-

sults in Appendixes II and III provide analysis of variance and multiple

comparisons based on subgroup analysis. Surprisingly, the results reveal

that there are signicant variations among experience groups (6 years, 6

to 10 years, 11 to 15 years, and >15 years). However, there are signif-

icant differences in the majority of the variables based on the job posi-

tion groups (CPAs, board of directors, accountants, and academicians).

This could be due to adaptability and openness to change, role-specic

relevance, and uniform exposure across roles. Respondents with vary-

ing years of experience may be more accustomed to recent de-

velopments, while those with fewer years may be more open to change.

Role-specic relevance may also inuence perceptions, as professionals

across roles may perceive AI’s inuence similarly due to its broad ap-

plications. It is suggested that the integrating accounting and auditing

skills is crucial for achieving positive outcomes and managing risks in AI

atiz-Lazo and Boyns, 2004). Overall, the ndings highlight the

importance of personalised interventions or educational activities that

recognise nuanced variances in perceptions depending on experience

while also recognising the overall alignment of viewpoints across varied

professional professions. The rise of AI in the accounting industry has led

to a shift from lower-skilled arithmetic roles to higher-skilled roles,

highlighting the industry’s resilience and AI’s ability to enhance human

capabilities (B

atiz-Lazo and Boyns, 2004; Ogaluzor, 2019). This trans-

formation has led to the emergence of positions requiring advanced

analytical skills, critical thinking, and strategic decision-making. Pro-

fessionals are now embracing AI tools to provide more insightful

nancial analysis and strategic advice, creating new career opportu-

nities and specialization. The accounting industry’s growth highlights

the need for staff that can adapt to AI technologies, focusing on human

skills like creativity, problem-solving, and interpersonal

communication.

Discussion and implications

According to the present study’s ndings, Big Data technologies

enable accountants to efciently process and analyze huge amounts of

data. For instance, accountants can gain important insights, identify

patterns and trends, and make data-driven decisions using Big Data

analytics, which improves accounting processes. Furthermore, Big Data

has an important effect on audit reporting, planning, processes, and

procedures. The results indicate that auditors can use Big Data analytics

to extract relevant information from massive datasets, uncover potential

risks and abnormalities, streamline audit procedures, and improve

auditing processes’ accuracy, efciency, and effectiveness. Big Data also

reveals a positive relationship with Costing. This implies that the re-

spondents perceive that big data could be benecial for business orga-

nizations in cost aspects.

Since Big Data analytics provide signicant insights and improve

decision-making processes, they increase accounting experts’ perceived

usefulness. Big Data analytics may also help organizations gain accurate

and timely reporting, maintain regulatory compliance, and improve

overall reporting and taxation procedures. Hence, the analytics give

rms useful visions and predictive capabilities for strategic decision-

making and budgeting processes. In this sense, organizations may

improve forecast accuracy, uncover development possibilities, and

optimize resource allocation. Several studies (Brown-Liburd and

Vasarhelyi, 2015; Huerta and Jensen, 2017; Salijeni et al., 2019; Sun and

Vasarhelyi, 2018; Warren et al., 2015) also indicate that big data can

streamline the accounting and auditing tasks, enhance the accuracy and

Table 12

SEM Estimation – Indirect Effect Model – TAM Dimensions.

Path β STDEV T Stat P

Values

Ease of Use -> Accounting Education 0.138 0.052 2.677 0.008

Ease of Use -> Accounting Practices 0.218 0.078 2.802 0.005

Ease of Use -> Audit Reporting 0.204 0.075 2.722 0.007

Ease of Use -> Audit Planning 0.221 0.078 2.835 0.005

Ease of Use -> Audit Process 0.229 0.081 2.837 0.005

Ease of Use -> Auditing Practices 0.218 0.077 2.815 0.005

Ease of Use -> Costing 0.215 0.077 2.800 0.005

Ease of Use -> Intention to Use AI 0.107 0.090 1.193 0.234

Ease of Use -> Reporting &_Taxation 0.222 0.079 2.807 0.005

Ease of Use -> Strategic _Planning

&_Budgeting

0.218 0.077 2.818 0.005

Industry 4.0 readiness -> Strategic

_Planning &_Budgeting

0.214 0.045 4.729 0.000

Intention to Use AI -> Audit Reporting 0.589 0.066 8.950 0.000

Intention to Use AI -> Audit Planning 0.640 0.067 9.565 0.000

Intention to Use AI -> Audit Process 0.662 0.067 9.831 0.000

Intention to Use AI -> Costing 0.623 0.066 9.420 0.000

Intention to Use AI -> Reporting

&_Taxation

0.640 0.068 9.415 0.000

Intention to Use AI -> Strategic _Planning

&_Budgeting

0.630 0.067 9.453 0.000

Perceived _Usefulness -> Accounting

Education

0.216 0.055 3.901 0.000

Perceived _Usefulness -> Accounting

Practices

0.341 0.091 3.744 0.000

Perceived _Usefulness -> Audit Reporting 0.319 0.084 3.779 0.000

Perceived _Usefulness -> Audit Planning 0.346 0.092 3.781 0.000

Perceived _Usefulness -> Audit Process 0.358 0.093 3.857 0.000

Perceived _Usefulness -> Auditing

Practices

0.340 0.089 3.842 0.000

Perceived _Usefulness -> Costing 0.337 0.090 3.730 0.000

Perceived _Usefulness -> Reporting

&_Taxation

0.346 0.093 3.710 0.000

Perceived _Usefulness -> Strategic

_Planning &_Budgeting

0.341 0.092 3.722 0.000

A.A.H. Abdullah and F.A. Almaqtari

Journal of Open Innovation: Technology, Market, and Complexity 10 (2024) 100218

timeliness of reporting, improve the efciency and effectiveness of the

tasks, leading to more efcient and insightful accounting and auditing

performance.

It can be also inferred from the results that Big Data technologies add

to the usability of accounting systems and applications by simplifying

data management operations, providing intuitive visualization and

reporting capabilities, and increasing overall user experience. In the

same context, the ndings conrm that Big Data analytics serve as the

cornerstone for accounting AI technologies, giving the essential data for

training AI models and producing accurate forecasts. Organizations may

develop an atmosphere that encourages the adoption and use of AI in

accounting processes by exploiting Big Data. This is consistent with

(Brown-Liburd and Vasarhelyi, 2015; Cockcroft and Russell, 2018; Gepp

et al., 2018; Salijeni et al., 2019; Sledgianowski et al., 2017; Warren

et al., 2015).

With regard to cloud computing, it is exhibited in the results that

implementing the technology into accounting education can improve

the learning experience by boosting access to materials and tools,

creating collaborative learning opportunities, and modeling real-world

accounting scenarios. Accountants can benet from Cloud Comput-

ing’s scalable computing resources, remote data storage, and collabo-

rative platforms, which allow them to accomplish jobs more efciently,

work smoothly with team members, and access real-time nancial in-

formation from anywhere. Auditors also can use Cloud Computing

technologies to store and analyze large volumes of audit data, streamline

the reporting process, enhance the accuracy and timeliness of audit re-

ports, improve the efciency and effectiveness of audit planning pro-

cesses, perform data-intensive tasks, analyze large datasets, and

automate certain audit procedures, leading to more efcient and

insightful audits.

In the context of Costing, organizations can optimize their costing

practices by efciently processing and analyzing cost-related data

through Cloud Computing’s cost-effective computing resources and data

storage options. The argument supports (Bianchi and Sousa, 2016;

Faccia et al., 2019; Frank et al., 2019; Schumacher et al., 2016). Guan

et al. (2020), Issa et al. (2016), Sun and Vasarhelyi (2018) also indicate

that cloud computing improves the efciency and effectiveness of

business operations.

The effect of deep learning on accounting education could lead to

interpretation that students can develop practical skills and information

linked to the application of sophisticated technologies, automate tasks,

and receive individualized learning experiences by incorporating Deep

Learning techniques into accounting education. Deep Learning also has

a positive relationship with Accounting Practices. Deep Learning in-

creases the accuracy, efciency, and effectiveness of accounting opera-

tions by automating repetitive tasks, analyzing massive amounts of data,

and discovering patterns or abnormalities in nancial data. Deep

Learning improves audit reporting, audit planning, the audit process,

and auditing practices. Furthermore, Deep Learning algorithms can

analyze and interpret audit data, identify risks, and generate more ac-

curate audit reports, thus providing valuable insights to stakeholders

and enhancing the overall audit process.

The integration of AI technologies and accounting and auditing

expertise is crucial for mitigating risks and achieving benecial out-

comes (B

atiz-Lazo and Boyns, 2004). This collaboration improves ef-

ciency and accuracy, while ensuring data security, ethical AI

deployment, and regulatory compliance. Accountants play a vital role in

ensuring AI applications align with organizational values and avoiding

immoral decision-making. This holistic approach highlights the trans-

formative potential of AI in economic institutions and the importance of

a harmonious collaboration between AI technology and accounting and

auditing skills. Further, the AI governance ecosystem is facing chal-

lenges due to insufcient internal control implementation and under-

utilization of audit activities, particularly in large corporations. Percy

et al. (2021) advocate that the present ecosystem is imbalanced,

necessitating greater transparency via AI, sufcient documentation, and

process formalisation in order to facilitate internal audits and external

accreditation processes. In this context, Anderljung et al. (2023) indicate

that AI governance involves policies, legislation, and ethical frameworks

for responsible AI development and usage. It requires credible infor-

mation from external sources like audits and third-party research. The

ASPIRE framework outlines criteria for effective external scrutiny of

border LLMs including access, a searching approach, risk proportional-

ity, independence, resources, and expertise (Anderljung et al., 2023).

Accordingly, accounting and nance professionals can help address

these issues by improving data governance, enhancing data accuracy,

and enhancing risk assessment and management. They also contribute to

compliance, performance evaluation, nancial visibility, strategic risk

management, and a comprehensive approach to governance. Integrating

nancial specialists into AI governance can provide insights into the

nancial implications of AI investments, align with budgetary goals, and

provide a strategic advantage in navigating uncertainties associated

with AI adoption (Anderljung et al., 2023; Percy et al., 2021; Ogaluzor,

2019).

The integration of articial intelligence (AI) into accounting and

auditing processes has raised concerns about job displacement, espe-

cially in repetitive tasks (Chen and Wen, 2021; Zhang and Dafoe, 2019).

Initially, these concerns were about job losses in physical labor. How-

ever, as the industry expanded, new opportunities for higher-skilled

professionals emerged, creating a dynamic and knowledge-based ac-

counting sector. AI can automate tasks like math and data entry but also

allows professionals to focus on critical thinking, strategic

decision-making, and complex problem-solving (Ogaluzor, 2019). This

highlights the importance of ongoing professional development and

adaptation, as human expertise complements AI capabilities (Ba´tiz-Lazo

& Boyns, 2004; Ogaluzor, 2019). Thus, organizations and educational

institutions are crucial in preparing professionals for this transition

(Zhang and Dafoe, 2019).

Conclusion

The present study has investigated the impact of articial intelli-

gence, Industry 4.0, and Technology Acceptance Model (TAM) factors

on accounting and auditing methods. AI and Industry 4.0 readiness have

been used as independent variables, whereas accounting education and

auditing and accounting practices have been considered dependent

variables. Further, TAM dimensions have been considered as mediating

variables that mediate the relationship between AI and Industry 4.0

readiness and accounting and auditing practices. The research has un-

covered several signicant links between AI, Big Data, Cloud

Computing, Deep Learning, perceived ease of use, perceived utility,

intention to employ AI, and various areas of accounting and auditing

processes. Convenience and snowballing sampling methods have been

used to collect the data from various Saudi organizations, and the

sample size was determined using various techniques, yielding a nal

sample of 228 respondents.

The study found a positive relationship between AI and perceived

usefulness, implying that AI is considered benecial for enhancing ac-

counting and auditing methods in Saudi Arabia. The study also

concluded that ease of use inuences perceived utility and intention to

utilize AI, implying that user-friendly AI solutions makes accounting and

auditing more efcient and effective. According to the ndings, Big

Data, Cloud Computing, and Deep Learning have a statistically signi-

cant and positive association with accounting and auditing practices.

This positive relationship emphasizes the positive role of Big Data, Cloud

Computing, and Deep Learning in driving AI adoption in accounting and

auditing practices, underlining how these tools promote AI integration

and application in accounting and auditing practices.

The study also indicated how perceived ease of use inuences AI

adoption. Respondents who believe AI is easy to use are more likely to

use AI technology in their professional operations. Accordingly, Big

Data, Cloud Computing, and Deep Learning have a signicant impact on

A.A.H. Abdullah and F.A. Almaqtari

Journal of Open Innovation: Technology, Market, and Complexity 10 (2024) 100218

accounting processes because respondents perceive that they streamline

audit processes, optimize costing, and enhance decision-making.

The research contributes to the understanding of AI acceptance and

use in Saudi accounting and auditing practices. This study sheds light on

how these factors interact and inuence the adoption and deployment of

AI technology in this area. The research also adds to the existing body of

knowledge by emphasizing the importance of Big Data, Cloud

Computing, and Deep Learning in promoting AI adoption in accounting

and auditing procedures. The study emphasizes the importance of these

technologies in boosting the capabilities and efcacy of accounting and

auditing. Further, this research contributes to a better understanding of

the characteristics that allow for the successful integration and use of AI

in professional accounting and auditing procedures.

Another contribution of the study is its dealing of the relationship

between perceived ease of use and intent to utilize AI. The study em-

phasizes the importance of user-friendly tools and intuitive AI systems in

inuencing professionals’ intentions to adopt and employ AI technol-

ogy. This conclusion implies that efforts to design and implement user-

friendly AI systems can have a signicant impact on their adoption and

acceptance in accounting and auditing operations. Furthermore, the

study adds to the body of knowledge by giving implications for practi-

tioners, policymakers, and researchers.

The ndings of the current study have implications for policymakers,

accountants, auditors, educators, and other professionals. Implementing

AI can lead to increased efciency, accuracy, and decision-making ca-

pabilities in certain professional sectors. Therefore, the ndings of this

study can help rms improve their accounting and auditing operations

by leveraging AI technologies, Big Data analytics, Cloud Computing, and

Deep Learning tools. Adopting AI allows practitioners to automate

mundane activities, decrease human errors, and use advanced analytics

to glean important insights from massive amounts of data. This can lead

to higher audit quality, more competitiveness, and more useful services

for clients. Practitioners can investigate the use of these technologies to

efciently handle and analyze huge amounts of data, improve data se-

curity and accessibility via cloud-based platforms, and employ powerful

machine learning algorithms for predictive analysis and anomaly

detection. The ndings of this study can be used by policymakers to

create supportive frameworks and regulations that encourage the

adoption and integration of AI technology in the accounting and

auditing elds. This could include offering nancial incentives,

encouraging knowledge sharing and collaboration among practitioners,

and creating partnerships between industry and academics to support

domain-specic AI research and development. Policymakers can also

address possible issues connected with AI adoption, such as ethics, data

privacy, and cybersecurity, by enacting stringent rules and standards.

The implications are not limited to policymakers; the research im-

plications extend to accounting and auditing researchers. The ndings

open insights for future research, especially in emerging markets,

allowing academics to investigate additional aspects and settings con-

nected to AI adoption, including investigating the importance of busi-

ness culture, leadership support, and staff training in promoting

successful AI integration. Researchers might also investigate the impact

of articial intelligence on specic accounting and auditing functions,

such as fraud detection, risk assessment, and nancial reporting. Future

research on AI’s application in accounting and auditing methods should

use qualitative methods like case studies, interviews, and ethnographic

approaches to explore the specic challenges faced by professionals in

emerging economies. The study also encourages interdisciplinary

research collaborations. For example, accounting, computer science,

and researchers in other related elds can work together to create novel

AI solutions directed to the needs of the accounting and auditing area.

Cross-disciplinary study can improve knowledge of AI’s potential in

addressing difcult challenges faced by accounting and auditing pro-

fessionals, thereby contributing to the eld’s growth.

Despite the signicant ndings of the current study, it is important to

consider limitations that may impact generalizing the results due to

inherent limitations that should be taken into consideration. For

example, the ndings may not be generalizable globally as the study

investigates the perception of the respondents from different culture and

business settings. Future research could include a wide range of in-

dustries, organizational sizes, culture, and geographical regions in order

to capture differences in AI adoption and utilization. Furthermore, the

study concentrated on the current state of AI adoption in accounting and

auditing practices, despite the fact that the eld of AI is continuously

growing. Longitudinal studies could be considered in future research to

follow updates in AI adoption over time, providing a better under-

standing of the long-term inuence on performance, efciency, and

effectiveness. Moreover, the study explores the benets and opportu-

nities of AI adoption in accounting and auditing. However, it acknowl-

edges limitations, such as an optimism bias in the survey design and a

lack of questions addressing potential downsides. The study calls for a

more balanced assessment of AI-related concerns. The ad hoc survey,

with a convenience sample of 224, reveals a positivity bias, requiring

cautious interpretation. The ndings highlight the perceived benets of

AI, but further research is needed to explore potential obstacles and is-

sues for a more comprehensive understanding of AI’s impact on ac-

counting and auditing methods. Moreover, the survey involved

participants from various Saudi organizations, but it is important to

acknowledge that the sample composition may introduce a level of

respondent selection bias. This bias may be inuenced by factors such as

professional roles and a specic focus on individuals interested in AI

technology in accounting and auditing. This highlights the importance

of cautious interpretation and generalization of the study’s ndings and

suggests future research to reduce bias and increase diverse perspec-

tives. Future research endeavours may look into measures to reduce bias

and increase the inclusion of varied perspectives within the surveyed

community.

The current research focused primarily on the adoption and usage of

AI technologies in accounting and auditing operations. It did not,

however, go into detail on the obstacles and barriers that businesses may

experience during the implementation process. Hence, future research

could look into the organizational, technical, and cultural challenges to

AI adoption in various sectors. Understanding these issues can provide

signicant insights and aid companies in developing ways to address

problems.

Besides, the present research was limited to large organizations, and

the results may not apply to small and medium-sized enterprises (SMEs).

Future studies could thus look into the specic obstacles and opportu-

nities that SMEs encounter when adopting and implementing AI tech-

nologies in their accounting and auditing practices.

Another area for future investigation is the impact of AI on the ac-

counting and auditing workforce. While the study only briey

mentioned the function of human-machine interaction, more research

might look into the specic tasks and responsibilities that may be

impacted by AI adoption. Understanding the consequences for job po-

sitions, skill needs, and workforce dynamics can help rms manage the

transition and ensure that AI technologies are integrated smoothly.

Future research also may investigate incorporating qualitative methods

like ethnographic studies and surveys to understand the impact of AI

adoption in the accounting and auditing industries. Finally, the present

study explored the advantages and prospects connected with AI adop-

tion in accounting and auditing. Potential dangers and obstacles, such as

cybersecurity threats, data integrity issues, and regulatory compliance,

are good backgrounds for future research. Investigating these risks can

assist organizations in developing strong policies to mitigate any nega-

tive outcomes and ensuring the proper use of AI technologies. To sum

up, scholars can improve our grasp of the implications, problems, and

prospects of AI adoption in the accounting and auditing domain by

addressing these constraints and investigating these future research

paths. This knowledge can help practitioners, policymakers, and aca-

demics make informed decisions while also supporting the effective and

responsible use of articial intelligence technologies.

A.A.H. Abdullah and F.A. Almaqtari

Journal of Open Innovation: Technology, Market, and Complexity 10 (2024) 100218

Ethical Statement

Not Applicable because the research conducted for this paper did not

involve experimentation on animal or human subjects. Ethical consid-

erations and approvals were duly adhered to in the conduct of this

research.

CRediT authorship contribution statement

Faozi A. Almaqtari, Abdulwahid Ahmad Hashed Abdullah:

Conceptualization, Methodology, Resources, Software, Validation,

Visualization, Writing – original draft, Writing – review & editing,

Conceived and designed the experiments, Performed the experiments,

Analysed and interpreted the data, Contributed reagents, Materials,

Analysis tools or data, Wrote the paper. Faozi A. Almaqtari: Formal

analysis. Abdulwahid Ahmad Hashed Abdullah: Project

administration.

Declaration of Competing Interest

The authors declare no conict of interest. We, the authors of this

manuscript, declare that we have no conicts of interest to disclose. We

conrm that there are no nancial, professional, or personal relation-

ships that could be perceived as potentially inuencing the research

work or its interpretation. All authors have reviewed and agreed to this

conict of interest statement. We are committed to upholding the

principles of fairness, objectivity, and impartiality in our research and

publication process.

Acknowledgment

This project was supported by the Deanship of Scientic Research at

Prince Sattam bin Abdulaziz University under the research project #

2022/02/23557.

Declaration

We acknowledge the use of ChatGPT for proofreading and English

language corrections in this work.

Appendix I. Questionnaire survey

Thems Construct Acrynom Items / indicators

AI Big Data BIGD1 We have access to very large, unstructured or fast-moving accounting and auditing data to analyze

BIGD2 We combine external data with internal data to facilitate high-value analysis of our business environment

BIGD3 We are able to efciently prepare, classify and analyze data and evaluate it for accounting and auditing purposes

Deepl Learning DEEPL1 I use deep learning techniques and tools for image recognition

DEEPL2 I use deep learning techniques and tools for language analysis (natural language processing) and metadata mining

DEEPL3 I use deep learning techniques and tools for speech recognition

Cloud Computing CLOD1 Our services are based on cloud computing to process accounting and auditing data

CLOD2 We have invested in advanced network infrastructure and cloud services to store accounting and auditing data

CLOD3 We work to ensure that accounting and administrative data are secured from beginning to end using the latest

technologies

Industry 4.0 Industry 4.0 readiness IR4.0_1 Industry 4.0 Improves economic performance through integrated interconnection inside and outside the facility (for

example, increasing quality, increasing production exibility, etc.)

IR4.0_2 Industry 4.0 Reduces costs of operations and storage of goods

IR4.0_3 Industry 4.0 helps to share real-time information and synchronization in order to reduce process completion time

IR4.0_4 Industry 4.0 Fullls multiple orders (meeting consumer needs - mass customization, improving customer relationship

management) with less time and high efciency

TAM Perceived ease of use EASE1 It would be easy for me to become procient in using AI systems in accounting or auditing.

EASE2 I nd it exible to make AI systems do what I want them to do in accounting or auditing.

EASE3 I nd AI systems in accounting or auditing easy to use.

Percievd usefulness USEFUL1 Using AI technologies will make it easier and faster to perform my future work in accounting or auditing.

USEFUL2 Using AI techniques will improve my future job performance in accounting or auditing

USEFUL3 Using AI technologies will enhance my effectiveness and productivity in accounting or auditing tasks

USEFUL4 Our rm has a great deal of opportunity to try various AI tools

USEFUL5 Using AI tools would enhance auditing and accounting efciency

USEFUL6 Management is aware of the benets that can be achieved with the use of AI tools.

Intention to Use AIINT1 I intend to adopt articial intelligence techniques in accounting and auditing tasks.

AIINT2 I intend to consider articial intelligence techniques when performing accounting and auditing tasks

Accounting

education

Accounting education ITEDU1 To what extent the following themes are covered in the syllabus of your university degree:

ITEDU2 Basic concepts of articial intelligence

ITEDU3 Probabilistic thinking and decision trees

ITEDU4 Virtual Reality (VR)

ITEDU5 Industry 4.0

(continued on next page)

A.A.H. Abdullah and F.A. Almaqtari

Journal of Open Innovation: Technology, Market, and Complexity 10 (2024) 100218

(continued)

ITEDU1 IT Governance

Audting practices Audit planning AUDPLN1 We analyze big data and contract databases using articial intelligence

AUDPLN2 Data related to the client’s organizational structure, operating methods, accounting and nancial systems are key

inputs to the AI system

AUDPLN3 Articial intelligence is used to estimate the level of audit risk and calculate the audit fees and number of hours

Audit Process AUDPR1 Articial intelligence is used to:

AUDPR2 collect data to identify risk factors for fraud and illegal actions

AUDPR3 Verication processes to ensure that internal control is properly implemented

AUDPR4 Details tested 100% of operations at all times

AUDPR1 Continuous pattern recognition, anomalous operations, benchmarking, and graphing

Audit Reporting AUDREP1 AI uses a predictive model to estimate various identied risks that can be used to issue the audit report

AUDREP2 The audit report can be continuous (scaled from 1 to 100 for example) rather than categorical (clean, conservative,

negative, etc.)

Accounting

practices

Reporting and Taxation The company’s accounting information systems are supported by articial intelligence to contribute to:

REP&TAX1 Provide information related to nancial resources, lists of cash needs and future cash ows

REP&TAX2 Cash ow planning and tax management

REP&TAX3 Analyzing cash ows according to various activities and estimating taxes in a way that serves administrative

decision-making

Costing and Pricing The company’s accounting information systems are supported by articial intelligence to contribute to:

PLN&COS1 Provide accurate and timely accounting data and information that serve cost determination and pricing decisions

PLN&COS2 In planning and analyzing costs at the level of activities of administrative units and the product

PLN&COS3 Estimating budgets to rationalize nancial decisions.

Strategic Planing and

Budgeting

Articial intelligence is used in:

STRPLN1 Estimating budgets, forecasting and nancial planning in order to rationalize nancial decisions.

STRPLN2 Integration of accounting information systems with other business systems to provide information with predictive

power that helps management plan for the future.

STRPLN3 Accessing the best nancial and strategic planning decisions

Appendix II. Additional analysis (Group Comparison)

Variables Experience Job Position

Sum of Squares Mean Square F Sig. Sum of Squares Mean Square F Sig.

AI 6.384 2.128 3.721 .012 1.938 .646 1.092 .353

128.125 .572 132.570 .592

134.509 134.509

BIGD 5.005 1.668 3.167 .025 3.036 1.012 1.889 .132

117.999 .527 119.968 .536

123.004 123.004

CLOD 3.927 1.309 3.001 .031 1.829 .610 1.369 .253

97.702 .436 99.800 .446

101.630 101.630

DEEPL 3.679 1.226 2.126 .098 4.265 1.422 2.476 .062

129.225 .577 128.640 .574

132.904 132.904

IR4.0 14.285 4.762 10.882 .000 3.301 1.100 2.261 .082

98.010 .438 108.994 .487

112.295 112.295

EASE 9.209 3.070 5.555 .001 3.596 1.199 2.075 .104

123.789 .553 129.402 .578

132.998 132.998

USEFUL 10.476 3.492 7.052 .000 .495 .165 .306 .821

110.914 .495 120.895 .540

121.390 121.390

AIINT .361 .120 .231 .875 1.441 .480 .929 .427

116.881 .522 115.801 .517

117.242 117.242

ITEDU 7.091 2.364 3.370 .019 16.465 5.488 8.321 .000

157.119 .701 147.745 .660

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A.A.H. Abdullah and F.A. Almaqtari

Journal of Open Innovation: Technology, Market, and Complexity 10 (2024) 100218

(continued)

164.211 164.211

AUDPLN 1.343 .448 .950 .417 2.387 .796 1.706 .167

105.534 .471 104.490 .466

106.877 106.877

AUDPR 7.331 2.444 5.096 .002 2.053 .684 1.360 .256

107.427 .480 112.705 .503

114.759 114.759

AUDREP 1.498 .499 1.031 .380 1.397 .466 .960 .412

108.537 .485 108.638 .485

110.035 110.035

REP&TAX 4.244 1.415 3.186 .025 1.339 .446 .976 .405

99.475 .444 102.381 .457

103.719 103.719

PLAN& COS 6.372 2.124 4.147 .007 .101 .034 .063 .979

114.743 .512 121.014 .540

121.115 121.115

STRPLN 7.383 2.461 4.505 .004 1.463 .488 .851 .467

122.364 .546 128.284 .573

129.747 129.747

Notes: AI is articial Intelligence, BIGD is Big Data, CLOD is Cloud Computing, DEEPL is Deep Learning, IR4.0 is Industry 4.0 readiness, EASE is Ease of Use, USEFUL is

Perceived Usefulness, AIINT is Intention to Use AI, ITEDU is Accounting Education, AUDPLN Audit Planning, AUDPR is Audit Process, AUDREP is Audit Reporting,

REP&TAX is Reporting &_Taxation, PLN&COS is Costing, and STRPLN is Strategic _Planning &_Budgeting.

Appendix III. Additional analysis (Multiple Comparisons)

Experience Job Position

Dependent Variable MeanDif. Sig. 95% Conf. Int. Dependent Variable MeanDif. Sig. 95% Conf. Int.

Lowr. Upr. Lowr. Upr.

AIINT < 6 6 to 10 -.03 1.00 -.34 .29 CPA BODs and CEOs .05 .99 -.36 .45

11 to 15 -.29 .17 -.67 .08 ACT, IAUD/CFOs .13 .73 -.20 .47

> 15 -.51 .04 -1.00 -.01 Academicians -.13 .85 -.57 .30

6 to 10 11 to 15 -.27 .18 -.61 .07 BODs and CEOs ACT, IAUD/CFOs .09 .93 -.29 .46

> 15 -.48 .04 -.95 -.01 Academicians -.18 .74 -.64 .28

11 to 15 > 15 -.21 .71 -.72 .30 ACT, IAUD/CFOs Academicians -.27 .31 -.66 .13

BIGD < 6 6 to 10 -.14 .64 -.45 .17 CPA BODs and CEOs -.07 .96 -.46 .32

11 to 15 -.22 .37 -.58 .13 ACT, IAUD/CFOs .12 .75 -.19 .44

> 15 -.55 .02 -1.02 -.07 Academicians -.20 .58 -.61 .21

6 to 10 11 to 15 -.08 .91 -.41 .24 BODs and CEOs ACT, IAUD/CFOs .20 .48 -.16 .55

> 15 -.41 .09 -.86 .04 Academicians -.13 .87 -.57 .31

11 to 15 > 15 -.32 .32 -.81 .16 ACT, IAUD/CFOs Academicians -.32 .12 -.70 .05

CLOD < 6 6 to 10 .02 1.00 -.26 .30 CPA BODs and CEOs .25 .27 -.11 .60

11 to 15 -.26 .18 -.58 .07 ACT, IAUD/CFOs .05 .98 -.24 .33

> 15 -.30 .29 -.73 .14 Academicians .16 .69 -.21 .53

6 to 10 11 to 15 -.28 .08 -.58 .02 BODs and CEOs ACT, IAUD/CFOs -.20 .37 -.52 .12

> 15 -.32 .19 -.73 .09 Academicians -.09 .94 -.49 .31

11 to 15 > 15 -.04 1.00 -.48 .40 ACT, IAUD/CFOs Academicians .11 .83 -.23 .46

DEEPL < 6 6 to 10 -.21 .35 -.53 .11 CPA BODs and CEOs -.03 1.00 -.43 .37

11 to 15 -.27 .24 -.64 .10 ACT, IAUD/CFOs -.12 .79 -.44 .21

> 15 -.42 .13 -.91 .08 Academicians -.42 .05 -.85 .00

6 to 10 11 to 15 -.06 .96 -.41 .28 BODs and CEOs ACT, IAUD/CFOs -.09 .92 -.45 .28

> 15 -.21 .65 -.69 .26 Academicians -.39 .12 -.85 .06

11 to 15 > 15 -.15 .88 -.66 .36 ACT, IAUD/CFOs Academicians -.31 .18 -.70 .09

IR4.0 < 6 6 to 10 -.10 .77 -.38 .17 CPA BODs and CEOs .21 .46 -.16 .58

11 to 15 -.63 .00 -.95 -.30 ACT, IAUD/CFOs -.02 1.00 -.32 .28

> 15 -.50 .01 -.94 -.07 Academicians -.21 .52 -.60 .18

6 to 10 11 to 15 -.52 .00 -.82 -.22 BODs and CEOs ACT, IAUD/CFOs -.23 .29 -.57 .11

> 15 -.40 .06 -.81 .01 Academicians -.42 .05 -.84 .00

11 to 15 > 15 .12 .89 -.32 .57 ACT, IAUD/CFOs Academicians -.19 .54 -.55 .17

EASE < 6 6 to 10 -.24 .22 -.55 .08 CPA BODs and CEOs .27 .31 -.13 .67

11 to 15 -.54 .00 -.90 -.17 ACT, IAUD/CFOs .20 .39 -.13 .53

> 15 -.49 .05 -.97 .00 Academicians -.07 .97 -.50 .36

6 to 10 11 to 15 -.30 .09 -.64 .03 BODs and CEOs ACT, IAUD/CFOs -.07 .97 -.43 .30

> 15 -.25 .49 -.72 .21 Academicians -.34 .22 -.79 .12

11 to 15 > 15 .05 .99 -.45 .55 ACT, IAUD/CFOs Academicians -.27 .28 -.67 .12

USEFUL < 6 6 to 10 -.24 .15 -.54 .06 CPA BODs and CEOs .03 1.00 -.36 .42

11 to 15 -.57 .00 -.92 -.23 ACT, IAUD/CFOs -.05 .97 -.37 .27

> 15 -.52 .02 -.98 -.06 Academicians -.12 .88 -.53 .29

6 to 10 11 to 15 -.33 .04 -.65 -.01 BODs and CEOs ACT, IAUD/CFOs -.08 .94 -.43 .28

> 15 -.28 .36 -.72 .16 Academicians -.15 .83 -.59 .30

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A.A.H. Abdullah and F.A. Almaqtari

Journal of Open Innovation: Technology, Market, and Complexity 10 (2024) 100218

(continued)

Experience Job Position

11 to 15 > 15 .05 .99 -.42 .52 ACT, IAUD/CFOs Academicians -.07 .97 -.45 .31

AIINT < 6 6 to 10 .01 1.00 -.29 .32 CPA BODs and CEOs .12 .85 -.26 .50

11 to 15 .05 .98 -.30 .41 ACT, IAUD/CFOs .10 .85 -.21 .41

> 15 .14 .87 -.33 .61 Academicians .26 .35 -.14 .66

6 to 10 11 to 15 .04 .99 -.29 .37 BODs and CEOs ACT, IAUD/CFOs -.02 1.00 -.37 .32

> 15 .13 .89 -.33 .58 Academicians .14 .84 -.29 .57

11 to 15 > 15 .09 .97 -.40 .57 ACT, IAUD/CFOs Academicians .16 .67 -.21 .53

ITEDU < 6 6 to 10 -.21 .40 -.57 .14 CPA BODs and CEOs -.68 .00 -1.11 -.25

11 to 15 .25 .41 -.17 .66 ACT, IAUD/CFOs -.61 .00 -.96 -.26

> 15 -.09 .98 -.63 .46 Academicians -.33 .24 -.79 .12

6 to 10 11 to 15 .46 .01 .08 .84 BODs and CEOs ACT, IAUD/CFOs .07 .97 -.33 .46

> 15 .13 .92 -.40 .65 Academicians .34 .26 -.14 .83

11 to 15 > 15 -.33 .42 -.90 .23 ACT, IAUD/CFOs Academicians .28 .32 -.14 .70

AUDPLN < 6 6 to 10 -.14 .59 -.43 .15 CPA BODs and CEOs .30 .14 -.06 .66

11 to 15 -.21 .38 -.55 .13 ACT, IAUD/CFOs .08 .88 -.21 .38

> 15 -.09 .96 -.54 .36 Academicians .18 .60 -.20 .57

6 to 10 11 to 15 -.07 .94 -.38 .24 BODs and CEOs ACT, IAUD/CFOs -.21 .34 -.54 .12

> 15 .05 .99 -.38 .48 Academicians -.11 .89 -.52 .30

11 to 15 > 15 .12 .90 -.34 .58 ACT, IAUD/CFOs Academicians .10 .89 -.25 .45

AUDPR < 6 6 to 10 -.10 .81 -.39 .19 CPA BODs and CEOs .29 .18 -.08 .67

11 to 15 -.48 .00 -.82 -.14 ACT, IAUD/CFOs .12 .73 -.18 .43

> 15 -.29 .35 -.74 .16 Academicians .12 .86 -.28 .52

6 to 10 11 to 15 -.38 .01 -.69 -.06 BODs and CEOs ACT, IAUD/CFOs -.17 .57 -.51 .17

> 15 -.19 .67 -.62 .24 Academicians -.17 .73 -.60 .25

11 to 15 > 15 .19 .72 -.28 .65 ACT, IAUD/CFOs Academicians .00 1.00 -.37 .37

AUDREP < 6 6 to 10 .01 1.00 -.29 .30 CPA BODs and CEOs .11 .86 -.26 .48

11 to 15 -.19 .50 -.53 .16 ACT, IAUD/CFOs -.03 1.00 -.33 .27

> 15 -.13 .89 -.58 .33 Academicians .18 .64 -.21 .57

6 to 10 11 to 15 -.19 .39 -.51 .12 BODs and CEOs ACT, IAUD/CFOs -.14 .70 -.48 .20

> 15 -.13 .86 -.57 .30 Academicians .07 .98 -.35 .48

11 to 15 > 15 .06 .99 -.41 .53 ACT, IAUD/CFOs Academicians .21 .45 -.15 .57

REP& TAX < 6 6 to 10 .05 .97 -.23 .33 CPA BODs and CEOs .15 .70 -.21 .51

11 to 15 -.30 .08 -.63 .03 ACT, IAUD/CFOs -.05 .97 -.34 .24

> 15 -.05 .99 -.48 .39 Academicians .08 .94 -.30 .46

6 to 10 11 to 15 -.35 .02 -.65 -.05 BODs and CEOs ACT, IAUD/CFOs -.20 .38 -.53 .12

> 15 -.09 .93 -.51 .32 Academicians -.07 .97 -.47 .34

11 to 15 > 15 .25 .46 -.19 .70 ACT, IAUD/CFOs Academicians .13 .76 -.22 .48

PLAN& COS < 6 6 to 10 .04 .98 -.26 .34 CPA BODs and CEOs .04 .99 -.35 .43

11 to 15 -.38 .03 -.73 -.03 ACT, IAUD/CFOs .02 1.00 -.30 .34

> 15 -.16 .82 -.63 .31 Academicians -.03 1.00 -.44 .39

6 to 10 11 to 15 -.42 .01 -.74 -.10 BODs and CEOs ACT, IAUD/CFOs -.02 1.00 -.38 .33

> 15 -.20 .66 -.65 .25 Academicians -.07 .98 -.51 .37

11 to 15 > 15 .22 .64 -.26 .70 ACT, IAUD/CFOs Academicians -.05 .99 -.42 .33

STRPLN < 6 6 to 10 -.05 .98 -.36 .27 CPA BODs and CEOs .19 .59 -.21 .60

11 to 15 -.46 .01 -.83 -.10 ACT, IAUD/CFOs .03 .99 -.29 .36

> 15 -.20 .71 -.68 .28 Academicians .18 .68 -.24 .61

6 to 10 11 to 15 -.42 .01 -.75 -.08 BODs and CEOs ACT, IAUD/CFOs -.16 .66 -.53 .20

> 15 -.15 .83 -.61 .31 Academicians -.01 1.00 -.47 .44

11 to 15 > 15 .27 .51 -.23 .76 ACT, IAUD/CFOs Academicians .15 .76 -.24 .54

Notes: AI is articial Intelligence, BIGD is Big Data, CLOD is Cloud Computing, DEEPL is Deep Learning, IR4.0 is Industry 4.0 readiness, EASE is Ease of Use, USEFUL is

Perceived Usefulness, AIINT is Intention to Use AI, ITEDU is Accounting Education, AUDPLN Audit Planning, AUDPR is Audit Process, AUDREP is Audit Reporting,

REP&TAX is Reporting &_Taxation, PLN&COS is Costing, and STRPLN is Strategic _Planning &_Budgeting

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