Journal of

Risk and Financial

Management

Article
Predicting Risk of and Motives behind Fraud in Financial
Statements of Jordanian Industrial Firms Using Hexagon Theory
Ahmad Ahed Bader 1, * , Yousef A. Abu Hajar 1 , Sulaiman Raji Sulaiman Weshah 2 and Bisan Khalil Almasri 3

1 Department of Financial and Administrative Sciences, Aqaba University College, Al-Balqa Applied
University, Aqaba 77110, Jordan; yousef.abuhajar@bau.edu.jo
2 Department of Accounting and Accounting Information System, Amman University College for Financial
Administrative Sciences, Al-Balqa Applied University, Amman 11831, Jordan; sulaiman.weshah@bau.edu.jo
3 Accounting Department, Business School, Applied Science Private University, Abu Nussair,
Amman 11937, Jordan; b_almasri@asu.edu.jo
* Correspondence: aaab@bau.edu.jo

Abstract: This study intends to identify the motives that lead to increasing or fighting the fraud
risk in the Financial Statements (FSs) of industrial companies whose shares are traded in regulated
and unregulated markets at the Amman Stock Exchange (ASE) based on the Hexagon theory, which
divides the motives for fraud into six factors. The study relied on secondary data to collect and
measure the study variables by extracting them from the annual reports that were published by
those companies on the website of the ASE during the period of 2012–2017. The collected data were
analyzed using the logistic regression model on the SPSS program. The results confirmed that the
return on assets (ROA), percentage of independent members in audit committees, and tone-related
party transactions had a statistically significant relationship with predicted fraudulent FSs, where
these three variables belong to pressure, opportunity, and collusion fraud motives, respectively. Thus,
it is worth mentioning that this study is distinguished from previous studies that examined the issue
of fraud in Jordanian companies by detecting the motives of fraud according to the Fraud Hexagon
Citation: Bader, Ahmad Ahed, Yousef
theory. Moreover, some of the fraud motives were measured using new variables such as a change in
A. Abu Hajar, Sulaiman Raji Sulaiman
inventory, the age of auditing committee’s members, and tone-related party transactions.
Weshah, and Bisan Khalil Almasri.
2024. Predicting Risk of and Motives
Keywords: F-score model; financial statement fraud; fraud hexagon theory; capital market; develop-
behind Fraud in Financial Statements
ing country; corporate governance
of Jordanian Industrial Firms Using
Hexagon Theory. Journal of Risk and
Financial Management 17: 120.
https://doi.org/10.3390/
jrfm17030120 1. Introduction

Academic Editor: Fernando

Accounting, as a profession, has witnessed significant development, facilitated by
Oliveira Tavares
international accounting organizations. Entities like the International Accounting Stan-
dards Board (IASB) issue standards to regulate the profession, providing a foundation for
Received: 28 January 2024 the work of accountants and ensuring the reliability of Financial Statements (FSs). The
Revised: 1 March 2024 adoption of these standards, known as International Financial Reporting Standards (IFRS),
Accepted: 10 March 2024
by many countries has contributed to their high reliability with financial market officials,
Published: 15 March 2024
often mandating listed companies to adhere to IFRS for FSs preparation. Despite these
advancements, the accounting and auditing profession still grapples with global accounting
fraud, leading to substantial annual losses for companies and eroding investor confidence.
Copyright: © 2024 by the authors. The annual loss ratio incurred by companies stands at 5% of their revenues, with
Licensee MDPI, Basel, Switzerland. fraudulent cases costing more than USD 3.6 billion (ACFE 2022, p. 8), underscoring the
This article is an open access article substantial impact of fraud. This not only results in financial losses but also undermines
distributed under the terms and investor confidence, as seen in the case of Enron (Bao et al. 2020). ACFE reports indicate
conditions of the Creative Commons a 49% increase in corporate fraud incidents from 2014 to 2022 (ACFE 2014, 2022). To
Attribution (CC BY) license (https:// effectively combat fraud, understanding its characteristics, strengthening internal control
creativecommons.org/licenses/by/ systems, and diffusing an integrity culture into companies are essential. This study aims
4.0/).

J. Risk Financial Manag. 2024, 17, 120. https://doi.org/10.3390/jrfm17030120 https://www.mdpi.com/journal/jrfm

J. Risk Financial Manag. 2024, 17, 120 2 of 27

to contribute to this understanding, particularly in the context of Jordan’s Amman Stock

Exchange (ASE), a developing market that is susceptible to financial fraud.
Previous studies examining fraud among companies that are listed on the ASE have
confirmed the use of fraudulent methods, including income smoothing, earnings manage-
ment, and FS fraud (Saleh et al. 2021; Al-Natsheh and Al-Okdeh 2020; Alrjoub et al. 2021;
Al-Daoud et al. 2023). The existence of such studies sheds light on the motives behind
fraudulent practices and aids in efforts to combat and mitigate them effectively.
Based on governance rules and international auditing standards, the preparation of
FSs that are free of fundamental errors is the responsibility of accountants and management
within companies. Hence, it is imperative for companies to establish robust internal control
systems that are capable of effectively and efficiently managing the risk of fraud. Auditing
standards emphasize not only the importance of internal controls but also underscore the
role of external auditors in formulating plans and assessing the risk of fraud to reason-
ably ensure the absence of fraud or misrepresentation in FSs during the auditing process
(DeZoort and Harrison 2018).
However, a pertinent question arises: are these guarantees and measures sufficient to
curb the phenomenon of fraud? This question gains significance in light of ongoing financial
fraud incidents within international companies, such as the case of Nisan, who uncovered
accounting errors perpetrated by the company’s chairman. These errors involved the
concealment of his actual income, totaling JPY 5 billion, which was not disclosed in the
company’s FSs (Shimamura 2023, p. 116).
The fraud in FSs underscores the importance of ongoing research in accounting studies.
Accountants, auditors, and financial managers bear the responsibility of managing the risks
associated with fraud detection (Tang and Karim 2019). However, the dynamic nature of
fraud tools and methods necessitates continuous skill development and vigilance (Tang
and Karim 2019). While international auditing standards address fraud in FSs, challenges
persist, especially in non-financial information, due to variations in auditors’ interpretation
and implementation of standards (Tang and Karim 2019). Therefore, auditors should
exploit the challenges that face fraud studies and turn them into achievable opportunities
in order to combat the fraud scourge of FSs (Amiram et al. 2018).
Numerous theories and models have been developed to explain the phenomenon of
financial fraud and identify its underlying causes. Some prominent examples include the
fraud triangle theory proposed by Cressey (1950), the fraud diamond theory introduced by
Wolfe and Hermanson (2004), the fraud pentagon theory presented by Marks (2012), and
the S.C.O.R.E Model outlined by Vousinas (2019), which extends to the S.C.C.O.R.E. model.
It is worth noting that the S.C.C.O.R.E model, by incorporating the collusion motive, results
in what is commonly referred to as the Fraud Hexagon theory, which encompasses six
motives for fraud.
Various theories, such as Fraud Hexagon and Crow’s fraud pentagon, aim to explain
financial fraud and the motives behind it. Despite their potential, the utilization of these
theories remains limited (Pamungkas et al. 2018). To address this, researchers have devel-
oped predictive models like the M-score (Beneish 1999) and F-score (Dechow et al. 2011) to
detect and predict FSs fraud. This study adopts the F-score to predict FSs with expected
fraud, employing Fraud Hexagon theory to identify underlying motives.
It is noteworthy that the F-score model comprises a series of questions, the outcome of
which is derived by inputting certain figures that are extracted from the FSs of the target
company to predict the likelihood of fraud. If the result of this model equals or falls below
1, it suggests a prediction of no fraud. Conversely, if the result surpasses 1, it implies a
prediction of potential fraud in the FSs of the company under examination. For further
elaboration, please refer to Section 3.2.
Conducted on a sample of listed and Over-The-Counter industrial companies in the
ASE from 2012 to 2017, this study holds significant implications for a developing country
like Jordan (Abdullahi and Mansor 2018). Understanding fraud characteristics and motives
is crucial for decision-makers and market regulators, as it assists in reducing fraud occur-
J. Risk Financial Manag. 2024, 17, 120 3 of 27

rences and enhancing investor confidence. Reurink (2018) emphasized the importance of
identifying the impact of financial fraud and its methods on financial markets in developing
countries, examining regulatory and legal gaps that may increase the likelihood of fraud
and conducting further studies on financial fraud.
Reviewing previous studies on fraud motives reveals significant disparities in the
measurement and outcomes of these motives. This highlights a challenge in generalizing the
results of prior studies on fraud motives to all countries, given the distinct economic, legal,
and cultural conditions in each nation. Consequently, these variations among countries serve
as an impetus for researchers to delve into the motives behind fraud, contributing valuable
insights to update laws and procedures that are aimed at combating fraudulent activities.
Moreover, the motives for companies engaging in fraud within the same sector are
subject to change over different periods. This becomes evident when comparing the
results of three studies that investigated fraud motives using the Hexagon Fraud theory
for industrial companies that were listed on the Indonesian Stock Exchange. For instance,
the study conducted by Tarjo and Sakti (2021) analyzed fraud motives from 2010 to 2018,
revealing statistically significant relationships between fraud and the pressure motive
(measured by the return on assets (ROA), leverage, and change in assets ratios), opportunity
motive (measured by the change in the account receivables ratio), and arrogance motive
(measured by CEO duality).
Similarly, Alfarago et al. (2023) examined fraud motives from 2015 to 2019, with
results indicating that only the pressure motive (measured by the change in total assets
ratio) had a statistically significant association with fraudulent FSs. Ultimately, Sikarini and
Kurniawati’s (Sikarini and Kurniawati 2023) study suggests that the rationalization motive
(measured by audit opinion) and pressure motive (measured by the change in total assets
ratio) were the predominant motives for industrial companies engaging in fraudulent FSs
from 2019 to 2021.
The current study is exploratory in nature, aiming to uncover the motives behind
fraudulent practices among listed and Over-the-Counter industrial companies on the ASE
during the period from 2012 to 2017. Achieving this objective contributes to understanding
the motives for fraud for researchers, decision-makers, users of FSs, and auditors. This
understanding enables them to manage the risk of fraud more effectively, particularly given
the limited research in this area that has been applied to industrial companies listed on
the ASE.
Moreover, a review of previous studies applied to Jordanian companies reveals that
these studies primarily relied on the triangle theory to uncover fraud motives, rather than
utilizing the Hexagon theory. In contrast, studies that were conducted in countries other
than Jordan to uncover fraud motives according to the Hexagon theory show variations
in results due to differences in the variables that were used to measure these motives.
Therefore, this study incorporates additional variables to measure fraud motives, such as
changes in the inventory, the age of auditing committee members, and the tone of related
party transactions. By including these variables, the study aims to broaden researchers’
perspectives to capture fraud motives more comprehensively, particularly given the existing
contradictions in the results of prior studies.
The study findings revealed that ROA (pressure motive), the percentage of indepen-
dent members in audit committees (opportunity), and tone-related party transactions (col-
lusion) exhibited statistically significant relationships with predicted fraudulent FSs. This
outcome bears significant implications for decision-makers, investors, and auditors alike.
For instance, auditors should broaden the scope of sample points and the depth
of evidence related to ROA components including revenues, expenses, and assets. They
should meticulously scrutinize agreements and contracts that are entered into by companies
with related parties, exercising a high degree of professional skepticism and reasonableness.
Additionally, auditors should reduce the accepted level of risk for accounts of this nature.
Moreover, the study’s results equip investors with enhanced analytical capabilities,
empowering them to utilize the F-score model to forecast fraud in a company’s financial
J. Risk Financial Manag. 2024, 17, 120 4 of 27

reports and incorporate it into their risk assessment for investment decisions. Furthermore,
decision-makers are advised to consider amendments to governance regulations and elevate
the proportion of independent members within companies’ audit committees based on
these findings.
In conclusion, the scourge of fraud persists, resulting in substantial losses for compa-
nies and investors, thereby negatively impacting national economies and investor confi-
dence. This underscores the need for heightened efforts by stakeholders to mitigate and
combat fraud effectively. Moreover, there is a pressing need for further research on fraud in
financial reporting to deepen stakeholders’ understanding of this pervasive issue. By ex-
panding knowledge and awareness, stakeholders can better address the challenges that are
posed by fraud and work towards building more resilient and transparent financial systems.
This paper is structured into six sections—an introduction, literature review, research
materials, results, discussion, and conclusion—and suggestions for future studies.

2. Literature Review and Hypothesis Development

Numerous studies and theories have emerged to comprehend fraud, its various types,
and the motivations driving individuals to engage in fraudulent activities. An enhanced
understanding of the fraud phenomenon empowers lawmakers and decision-makers,
facilitating more effective anti-fraud measures and contributing to the overall economic
well-being of nations.
Fraud in FSs manifests through manipulations of accounting figures, omissions of
financial processes, or incorrect applications of accounting principles, leading to errors
or misrepresentations in FSs (Pramana et al. 2019). Given that FSs are crucial products
influencing investor decisions, any manipulation or fraud by accountants and financial
managers harms investor interests, erodes confidence, and deters investments in capital
markets upon discovery (Md Nasir et al. 2018). Despite regulatory and market participants’
efforts to detect and prevent fraud in FSs, it remains a challenging task (Li and Yang 2019).
The prosperity of financial markets hinges on the transparency and reliability of companies’
FSs. While auditors’ opinions contribute to enhancing FSs’ reliability, fraud persists, emphasizing
the need for auditors to play a more proactive role in reducing fraud (Kizil and Kasbasi 2018).
External auditors, who are responsible for ensuring fair presentation of FSs, have a limited
role in fraud detection, as evidenced by the 4% discovery rate of fraud cases (ACFE 2022;
Rustiarini et al. 2021). Relying solely on external auditors for fraud detection is insufficient,
necessitating additional methods to bolster anti-fraud procedures.
Understanding the motives behind fraud in companies is essential for developing
effective anti-fraud measures. Scholars have proposed various theories to grasp the fraud
phenomenon. Cressey’s fraud triangle theory from the 1950s identified three motives: pres-
sure (financial obligation), opportunities, and justification (rationalizations) (Cressey 1950).
Wolfe and Hermanson (2004) expanded on this with the diamond fraud theory, introducing
a fourth element, ability. Marks (2012) added efficiency and arrogance to create the fraud
pentagon theory. Vousinas (2019) further extended this with the Fraud Hexagon theory,
introducing collusion as the sixth element.
In the previous paragraph, the evaluation of fraud theories related to motives was
discussed, highlighting the Hexagon theory that was developed by Vousinas in 2019, which
comprises six motives. In this paragraph, the meanings of these motives will be briefly
elucidated, along with how they contribute to incidents of fraud in companies.
The pressure motive arises when companies or individuals face circumstances that
compel them to commit fraud such as financial constraints or the need to meet expectations
that are placed upon them. Drawing on the case of fraud perpetrated by Enron, we observe
that the company manipulated its profits to maintain a favorable financial performance,
meeting the shareholders’ expectations (Tebogo 2011). Consequently, investors’ aspirations
and their anticipations of positive outcomes from the company exerted pressure on its
management to engage in fraudulent activities.
J. Risk Financial Manag. 2024, 17, 120 5 of 27

Regarding the motive opportunity, the absence of effective oversight and its inherent
weakness create opportunities for individuals and companies to engage in fraudulent
behavior. When a person inclined toward fraud recognizes an opportunity in the absence
of moral constraints, they are likely to exploit it for personal gain without regard for others.
For instance, in the case of Worldcom, which collapsed due to financial scandals, one of
the contributing factors was the inadequate oversight by the board of directors over the
company’s CEO. This lack of oversight provided the CEO with an opportunity to perpetrate
fraud and manipulate the company’s accounts (Çakali 2022).
The motive of collusion arises from the involvement of a group of individuals in
deceiving and defrauding others, often through coordinated agreements that are aimed
at deceit. Transactions involving related parties can sometimes be deceptive agreements
that undermine the interests of stakeholders. In the case Worldcom, it is evident that the
company’s CEO borrowed USD 400 million at a low competitive interest rate to finance
personal interests and settle personal debts, further exacerbating the company’s list of
financial scandals and manipulations (Çakali 2022). Consequently, this transaction deprived
the company’s shareholders of potential revenue that could have been generated if the
funds had been invested in the company’s operations.
Competence motivation arises when individuals possess the capability to commit
fraud due to their skills, knowledge, and values, which enable them to engage in fraudulent
activities. In the case of the fraud incident at Tyco, two executives within the company
perpetrated the fraud by leveraging their authority, abilities, and expertise to exploit the
company’s resources for fraudulent purposes (Therese and Jakobsen 2008, p. 17).
The motive of arrogance arises when an individual, endowed with power and au-
thority, feels above the laws and procedures that have been established by the company,
prompting them to engage in fraudulent, manipulative, and exploitative behavior. In the
case of the fraud incident at Tyco, investigators revealed that the company’s CEO purchased
homes for his personal use through the company’s loan program, subsequently selling
these homes to some of the company’s subsidiaries at prices three times higher than their
market value (Therese and Jakobsen 2008, p. 18) This highlights how the authority vested
in the CEO of Tyco and the exploitation of his position contributed to fraudulent activities
being carried out within the company.
The motive of rationalization emerges when a fraudulent individual justifies to them-
selves why they committed fraud, believing that they deserve the gains that were obtained
through fraudulent means due to their rationalization. This was evident in the case of the
fraudsters at Adelphia, where the company faced challenging financial conditions, leading
to the distortion of FSs to portray the company as performing well. This manipulation was
rationalized by asserting that it would be rectified once the company emerged from its
financial crises (Therese and Jakobsen 2008, p. 29).
As previously mentioned, the motives according to the Fraud Hexagon theory include
pressure, opportunity, rationalization, arrogance, competence, and collusion. Thus, the
following sub-section presents the study hypotheses that aligned with these six elements
as follows: Hypotheses 1–5 with pressure, 6–9 with opportunity, 10 with rationalization, 11
with arrogance, 12 with competence, and 13 with collusion.

2.1. Pressure
The size of a company’s assets holds significant importance for investors and lenders,
often serving as a key factor in their decision-making process. A larger asset size is generally
perceived as an indicator of financial stability (Rengganis et al. 2019; Alfarago et al. 2023).
Moreover, companies with substantial asset sizes are subject to higher expectations from in-
vestors and creditors anticipating these to yield substantial returns (Puspitha and Yasa 2018).
The robust financial standing and the stakeholders’ anticipation of favorable returns from
companies with significant assets exert considerable pressure on the management of such
companies. This pressure, in turn, may drive company mangers to engage in fraudulent
J. Risk Financial Manag. 2024, 17, 120 6 of 27

activities, manipulating asset sizes to align with the anticipated expectations of investors
and creditors (Novira and Kurnia 2018; Puspitha and Yasa 2018; Rengganis et al. 2019).
Therefore, the aforementioned insights can be justified by recognizing that the mag-
nitude of assets within companies poses significant considerations for decision-makers.
These considerations create pressure on the company to uphold the size of its assets and
potentially inflate them through fraudulent means, utilizing asset valuation tools inap-
propriately. In light of these considerations and justifications, researchers have suggested
that an uptick in the rate of change in a company’s total assets intensifies the pressure on
the company’s management to engage in manipulation and fraudulent practices in FSs.
Consequently, the first hypothesis is formulated as follows:

Hypothesis 1. The change ratio in the total amount of assets (%∆TA) has a positive effect on FSs
being predicted to be fraudulent.

As previously highlighted, the financial stability of companies is identified as one of

the factors that may drive company managers to engage in fraudulent activities in FSs. In
this study, researchers aim to gauge financial stability using an additional indicator: the
asset turnover rate. The chosen indicator assesses a company’s efficiency in utilizing its
assets to generate revenues (Puspitha and Yasa 2018). When users of FSs compare the asset
turnover rates of competing companies, a lower turnover rate prompts pressure on the
firm’s management to enhance this metric, potentially leading to manipulations in FSs
(Putra 2015).
The utilization of this indicator can be justified owing to its significance to investors,
as it provides insight into the company assets’ capacity to generate revenue. A higher
turnover rate typically makes the company more able to generate revenue, potentially
leading to fraudulent activities.
In summary, a decrease in the asset turnover rate of companies is anticipated to
heighten the pressure on these companies and elevate the likelihood of manipulations in
FSs. Therefore, the second hypothesis is formulated as follows:

Hypothesis 2. The total asset turnover (TA_Trn) has a negative impact on the FSs being predicted
to be fraudulent.

Organizations secure their capital from either shareholders’ funds or lenders to sup-
port their operational activities. Consequently, an augmentation in the debt component
within a company’s capital structure is associated with an increase in the credit risk that
is borne by the company (Puspitha and Yasa 2018; Achmad et al. 2022). An elevated
corporate credit risk diminishes the company’s borrowing capacity (Sunardi and Amin
2018). Consequently, companies facing a heightened credit risk and aspiring to secure
additional financing to bolster their competitiveness may experience intensified pressure.
This pressure compels the management of such companies to potentially manipulate their
FSs to present a favorable financial performance that is capable of meeting obligations to
creditors (Situngkir and Triyanto 2020).
In light of the aforementioned dynamics, any escalation in a company’s financial
leverage is posited to augment the pressure on the company’s management to engage in
fraudulent activities in FSs. Therefore, the third hypothesis is formulated as follows:

Hypothesis 3. The leverage ratio (LV) has a positive impact on the FSs being predicted to
be fraudulent.

Agency theory proposes that involving managers in the ownership of a company can
mitigate conflicts of interest. However, granting them a stake in the company’s ownership
may also expose them to allegations of insider trading. This dilemma becomes particularly
pronounced when managers require personal financing, creating a situation that places
pressure on them and may increase the likelihood of fraudulent activities in FSs (Rukmana
J. Risk Financial Manag. 2024, 17, 120 7 of 27

2018). The significant ownership stake that is held by managers in the company’s shares
becomes a substantial asset, influenced by the company’s performance. In time of financial
need, this situation may incentivize managers to engage in fraudulent activities in the FSs,
aiming to enhance the value of the shares that they possess in the company (Alhebri and
Al-Duais 2020; Amiram et al. 2018; Puspitha and Yasa 2018; Putra 2015).
This is what happened at Qwest Communications in 2001, when the company’s FSs
were manipulated, and its revenues were inflated. In the same year, the company’s financial
director and CEO carried out insider trading operations and sold the company’s shares,
achieving huge sums of money as a result of this operation (Stanwick and Stanwick 2009).
Consequently, it can be inferred that any increase in the managers’ ownership ratio
in the company is likely to correlate with an increase in the fraud ratio in the company’s
FSs. Therefore, the formulation of the fourth hypothesis, which pertains to the pressure
variable, is as follows:

Hypothesis 4. The size of the insider ownership ratio (Insi_Own) has a positive impact on the FSs
being predicted to be fraudulent.

Companies consistently strive to attain financial objectives which serve as a focal point
for numerous users of FSs, including investors, creditors, and other stakeholders who
evaluate the success of these enterprises. The management is inherently vested in realizing
these financial goals, as they are typically tied to the value of incentives and rewards
accruing to managers within the company (Pramana et al. 2019). Consequently, managers
face significant pressure to achieve the financial goals of their companies to maximize
the benefits that they receive, encompassing both incentives and rewards (Pamungkas
et al. 2018). This heightened pressure on management to meet financial objectives may, in
turn, create circumstances wherein managers are tempted to manipulate and engage in
fraudulent activities in the FSs (Sunardi and Amin 2018).
Various indicators are employed to gauge financial goals, with the ROA ratio standing
out as one of the most prominent indicators for assessing management’s efficiency in
leveraging assets to generate a return (Hung et al. 2017; Sikarini and Kurniawati 2023).
Numerous studies have affirmed that an escalation in the ROA corresponds to an uptick in
fraudulent activities in FSs. This phenomenon is attributed to the heightened pressure on
managers to manipulate FSs and enhance the ROA ratio (Dechow et al. 2011; Devi et al.
2021; Manurung and Hadian 2013; Rukmana 2018).
Ultimately, committing fraud in companies can be justified because their financial
indicators, such as the ROA, are the focus of many stakeholders’ attention, and therefore,
the management of companies may manipulate the accounts that make up the ROA to
appear attractive to investors and within their expectations. In light of the previous studies
and justifications, it can be deduced that any increase in the ROA is likely to be associated
with fraud and manipulation of the company’s FSs. Therefore, the fifth hypothesis is
formulated as follows:

Hypothesis 5. The return on assets ratio (ROA) has a positive impact on the FSs being predicted
to be fraudulent.

2.2. Opportunity
When discussing the industrial nature of companies, it signifies the optimal conditions
within the industrial environment, providing company management with the opportunity
to exercise personal judgments concerning accounts such as receivables and inventories
(Hidayah and Saptarini 2019; Putra 2015; Sikarini and Kurniawati 2023). As a result, the
industry’s nature contributes to an increase in the fraud rate in FSs (Hidayah and Saptarini
2019; Novira and Kurnia 2018; Puspitha and Yasa 2018; Rengganis et al. 2019; Rukmana
2018; Situngkir and Triyanto 2020). Researchers have employed variables associated with
inventories to measure the industry’s nature.
J. Risk Financial Manag. 2024, 17, 120 8 of 27

Many studies have predominantly focused on utilizing the change in receivables-to-

sales size as a measure of the industry’s nature in comparison to the variable associated
with the inventory size. Puspitha and Yasa’s study (Puspitha and Yasa 2018) highlighted
the significance of considering the change in inventory size as a measure of the industry’s
nature, demonstrating its positive impact on FS fraud. The challenge with the inventory
account lies in the fact that management must rely on personal estimates to evaluate it,
especially when dealing with a decrease in the inventory value. This situation provides
management with an opportunity to manipulate the estimated value of the inventory,
particularly in cases of goods accumulation and a subsequent decrease in their value (Putra
2015). Therefore, a higher growth rate of a company’s inventory size with a lower inventory
turnover rate increases the likelihood of fraud and manipulation of FSs (Hidayah and
Saptarini 2019; Putra 2015; Sikarini and Kurniawati 2023). Based on the above, the sixth
hypothesis is formulated as follows:

Hypothesis 6. The change growth in the inventory account (%∆Inven) has a positive impact on
the FSs being predicted to be fraudulent.

Fraud in FSs seizes opportunities whenever there is a weakness in a company’s internal

control system; managers may exploit these weaknesses to engage in fraudulent activities
in FSs (Situngkir and Triyanto 2020). To mitigate the likelihood of fraud in companies,
regulations and laws pertaining to corporate governance have begun to issue procedures
and requirements that aim at enhancing the reliability of FSs. One such requirement is
the establishment of an audit committee composed of members of the board of directors.
This committee serves to oversee financial operations and ensure that FSs remain free of
accounting fraud (Dewi and Anisykurlillah 2021; Larune et al. 2021). It is crucial to note
that the effectiveness of the control by the audit committee depends on the independence of
its members, a factor that significantly contributes to enhancing the reliability and integrity
of companies’ FSs (Situngkir and Triyanto 2020). As the independent member in an audit
committee is more free and impartial in the event that fraud is discovered, he will apply
the laws and take the necessary actions to combat fraud without bias if it is discovered
in the company. In contrast, the non-independent members have relationships with and
interests in the company that may lead them to cover up fraud if it occurs.
Therefore, a higher percentage of independent members in the audit committee cor-
responds to an increased effectiveness in controlling operations and procedures related
to the operation of FSs. This ultimately results in a reduction in the likelihood of fraud in
companies’ FSs. Thus, the relationship between the percentage of independent members in
the audit committee and fraud in FSs is negative (Md Nasir et al. 2019; Pramana et al. 2019;
Rengganis et al. 2019). Accordingly, the seventh hypothesis is formulated as follows:

Hypothesis 7. The percentage of independent members in an auditing committee (%Ind_AuCo)

has a negative effect on FSs being predicted to be fraudulent.

As was explained previously, the weakness of the internal control system in itself
constitutes an opportunity that may be exploited by fraudulent people, and the internal
control system may sometimes be linked to the age of the members of the audit committees,
as will be explained in the following paragraphs. There is also a scarcity of research applied
to developing countries regarding the age of audit committee members and the efficiency
of their performance (Hasnan et al. 2022).
The age variable is considered a factor that leads to changes in the personal qualities
of individuals. Pålsson (1996) pointed out that as people get older, they become more
sensitive to risks. This implies that older members of an auditing committee will have
greater sensitivity to risks, particularly in maintaining retirement income and safeguarding
their reputation, as their future job opportunities may decrease with age (Qi and Tian 2012;
Sultana et al. 2019). Consequently, older members of an auditing committee are likely to
adopt a conservative approach in the selection process of an external auditor and in making
J. Risk Financial Manag. 2024, 17, 120 9 of 27

decisions that enhance the transparency and integrity of financial reports (Qi and Tian 2012;
Sultana et al. 2019).
Age also plays a role in increasing the amount of accumulated experience among
auditing committee members. This experience enables them to address deficiencies in
the company’s internal control system (Qi and Tian 2012). Therefore, it is reasonable to
conclude that an increase in the age of auditing committee members will lead to greater
effectiveness in the control with and integrity of financial reports, thereby reducing the
chance of manipulation in FSs. Accordingly, the eighth hypothesis is formulated as follows:

Hypothesis 8. The age of auditing committee members (LgAuCo_Age) has a negative impact on
the FSs being predicted to be fraudulent.

A company with members of the board of directors holding multiple positions on other
companies’ boards is considered an indication of their good reputation in the professional
environment. Additionally, this reflects their extensive experience in strategic plans and
procedures that are carried out by the managers who are members of their boards of
directors (Puspitha and Yasa 2018; Zachro and Utama 2021). Consequently, members of the
board of directors with numerous memberships in other companies’ boards are expected to
demonstrate greater efficiency and effectiveness in controlling and supervising companies’
mangers. This heightened oversight reduces the opportunity for managers to engage in
fraudulent activities when preparing FSs (Premananda et al. 2019; Puspitha and Yasa 2018;
Zachro and Utama 2021).
From the above, it is reasonable to conclude that any increase in the percentage
of members on a company’s board of directors who hold multiple positions on other
companies’ boards will diminish the chances of fraud in FSs. Therefore, the ninth hypothesis
is formulated as follows:

Hypothesis 9. The percentage of members in the board of directors who have multiple positions
in boards of directors of others companies (%Dir_MulPo) has a negative impact on the FSs being
predicted to be fraudulent.

2.3. Rationalization
Justification is considered one of the motives for fraud and manipulation in FSs, which
is manifested when management rationalizes fraudulent or deceptive practices in FSs
(Hidayah and Saptarini 2019; Situngkir and Triyanto 2020). The process of preparing a
company’s financial reports falls under the responsibility of the company’s management,
which presents the business results to the users of FSs. Here, the role of the auditor is
crucial in instilling confidence and reasonableness regarding the financial reports that are
prepared by management and in detecting any fundamental errors resulting from fraud or
deception (Pramana et al. 2019).
Developments in business at the global level, in addition to financial deregulation, have
added more challenges for external auditors (Campa et al. 2023). Despite the importance of
the auditor’s role in detecting fraud, there are some restrictions that may limit his ability
to detect fraud or tolerate it, such as the fear of losing the company that he is assigned
to audit, the lack of data, and his lack of sufficient experience regarding the nature of the
business that is carried out by the company (Shwetha et al. 2023).
The failure of an auditor to detect fraud, deception, or manipulation in FSs serves as a
justification for management to engage in manipulation. This was evident in the collapse
of Enron, where the external auditor’s auditing process failed to uncover the manipulation
that was orchestrated by Enron’s management (Sunardi and Amin 2018). Manipulative
management may create a justification to frequently change external auditors, aiming to
reduce the chances of a new auditor detecting any manipulation and fraud (Hidayah and
Saptarini 2019; Pramana et al. 2019; Situngkir and Triyanto 2020). Consequently, companies
with high turnover rates in their external auditors are likely to experience an increase in
J. Risk Financial Manag. 2024, 17, 120 10 of 27

fraud in their FSs (Pamungkas et al. 2018). Based on the above, the tenth hypothesis can be
formulated as follows:

Hypothesis 10. The change of external auditor (ExAu_Swt) has a positive impact on the FSs
being predicted to be fraudulent.

2.4. Arrogance
The arrogance of Chief Executive Officers (CEOs) is considered a variable that con-
tributes to fraud, as arrogant CEOs may perceive themselves to be above the law and other
authorities (Hidayah and Saptarini 2019; Pamungkas et al. 2018; Sikarini and Kurniawati
2023). Consequently, the privileged position of arrogant CEOs fosters a sense of superiority
over the company’s internal control system, exempting them from accountabilities that
apply to others (Situngkir and Triyanto 2020). The power that is held by arrogant CEOs
propels them to engage in fraud and manipulation of FSs, as they believe themselves to be
beyond the reach of the law and internal control systems, thereby avoiding accountability.
Several studies, including by Rukmana (2018), Hidayah and Saptarini (2019), and
Alfarago et al. (2023) posit that the presence of multiple images of CEOs in annual financial
reports serves as an indication of CEO arrogance. Based on this, it can be concluded that
any increase in the number of CEOs pictures in a company’s financial reports will lead
to heightened CEO arrogance, serving as an indicator of potential fraud in the financial
reports. Therefore, the eleventh hypothesis is formulated as follows:

Hypothesis 11. The frequency number of CEO images (CEO_Pic) has a positive impact on FSs
being predicted to be fraudulent.

2.5. Competence
Competency is identified as another motive that contributes to fraud, signifying indi-
viduals’ abilities to circumvent company rules, mechanisms, and procedures that have been
established to ensure the integrity of FSs. Additionally, individuals with high competency
possess the capability to devise strategies aimed at concealing fraud and deception, lever-
aging their positions within the company for personal gain (Pamungkas et al. 2018; Sunardi
and Amin 2018; Sikarini and Kurniawati 2023). Auditing Standard Number Ninety–Nine
highlights that high turnover rates in senior positions within companies such as mem-
bers of the board of directors may indicate fraud and manipulation within the company
(Rukmana 2018).
When members of the board of directors utilize their positions to influence others and
facilitate fraudulent activities, companies tend to undergo changes in board membership
as a response to the ongoing fraud and manipulation (Situngkir and Triyanto 2020; Sunardi
and Amin 2018). The period during which a company undergoes changes in its board of
directors’ composition is considered critical in terms of increasing the likelihood of fraud
by senior management, as new members require more time to comprehend the company’s
internal operations (Pamungkas et al. 2018). Moreover, it may be that companies change
their directors as result of the failure of these directors to detect fraud if it occurs in the
company (Alfarago et al. 2023).
Based on the above, it is possible to conclude that the process of changing members of
the board of directors may serve as an indication of the existence of fraud and manipulation.
Therefore, the twelfth hypothesis is formulated as follows:

Hypothesis 12. Changing directors (Dir_Chg) has a positive impact on the FSs being predicted to
be fraudulent.

2.6. Collusion
Company management may engage in collusion with other parties to manipulate
and defraud FSs for personal interests. In recent years, numerous cases of FS fraud have
J. Risk Financial Manag. 2024, 17, 120 11 of 27

resulted in significant losses for companies due to management collusion (Handoko and
Tandean 2021). Collusion occurs when a group of individuals agree to undertake actions
and processes that deceive others and harm their interests, all while securing personal
benefits for those involved (Handoko and Tandean 2021). The standard AU-C-Section 550
on related parties, issued by the Auditing Standards Committee, indicates that the existence
of transactions with related parties in companies increases the likelihood of collusion and
manipulation in FSs by the company’s management (Auditing Standards Board 2021).
Numerous prior studies have highlighted that the presence of transactions involving
related parties increases the risk of manipulation and fraud. External auditors must
meticulously assess these transactions to ensure the absence of collusion and manipulation
in FSs by companies’ management (Jeppesen 2019; Kakati and Goswami 2019). Pozzoli
and Venuti (2014) clarified that transactions with related parties may either be based on
a commercial basis to serve the interests of the companies or rely on the exploitation of
companies’ economic resources, potentially causing harm to the companies’ interests. This
implies that not all transactions with related parties necessarily indicate fraud. Kohlbeck
and Mayhew (2017) affirmed this point by categorizing transactions with related parties into
the main groups of business-related party transactions and tone-related party transactions.
Their study revealed a relationship between tone-related party transactions and fraud,
unlike the business-related party group, which did not show a correlation with fraud. In
light of the above, it can be concluded that the presence of tone-related party transactions
increases the chances of collusion in companies’ FSs. Therefore, the thirteenth hypothesis is
formulated as follows:

Hypothesis 13. Tone-related party transactions (Tone_RPTs) have a positive effect on the FSs
being predicted to be fraudulent.

In conclusion, the hypotheses developed in this study aimed to encompass a wide

range of variables under each motive that could potentially be linked to fraud. The
study considered variables that have commonly been used by researchers in previous
studies, as well as variables proposed by them. For instance, in the second and sixth
hypotheses, the study incorporated variables recommended by researchers for future
investigations. Additionally, the study sought to incorporate variables related to the
characteristics of companies’ audit committees, such as age, which had been overlooked in
many previous studies.
Meanwhile previous studies have employed various variables to measure the motive
for collusion relationships within the company involving government entities or joint
projects. The current study introduced a new variable, tone-related party transactions,
to measure this motive, broadening its applicability beyond collusion with government
agencies. This expansion is crucial, especially considering that real cases of collusion are
not solely confined to transactions with government parties. For instance, in the fraud case
at Worldcom, the CEO borrowed funds from the company at a competitive interest rate,
which falls under tone-related party transactions and is indicative of financial fraud.
By incorporating multiple variables to measure certain fraud motives, including those
recommended in previous studies, and integrating variables that were demonstrated in
real fraud cases, the study contributes to a deeper understanding of the phenomenon of
fraud and aids in efforts to combat it effectively.
According to the literature review and constructed research hypotheses, the research
model of the current study is presented in Figure 1. Thus, it is worth noting that the (+)
symbol in Figure 1 assumes a positive relationship between the independent variable and
the dependent variable, whereas the (−) symbol proposes a negative relationship.
 demonstrated in real fraud cases, the study contributes to a deeper understanding of the
phenomenon of fraud and aids in efforts to combat it effectively.
According to the literature review and constructed research hypotheses, the research
model of the current study is presented in Figure 1. Thus, it is worth noting that the (+)
J. Risk Financial Manag. 2024, 17, 120 12 ofand
27
symbol in Figure 1 assumes a positive relationship between the independent variable
the dependent variable, whereas the (−) symbol proposes a negative relationship.

Figure1.1.Research
Figure Researchmodel
modelofofthe
thestudy.
study.

3.3.Research
ResearchMaterials
Materialsand
andMethods
Methods
3.1. Sample and Data Collection
3.1. Sample and Data Collection
The
The study sample consists
study sample consistsofoflisted
listed and
and non-listed
non-listed (Over-The-Counter)
(Over-The-Counter) industrial
industrial com-
companies on the ASE market during the years of 2012–2017. The rationale
panies on the ASE market during the years of 2012–2017. The rationale behind selecting behind selecting
in-
industrial companies stems from reports by the Association of Certified
dustrial companies stems from reports by the Association of Certified Fraud Examiners Fraud Examiners
(ACFE),
(ACFE), which indicatedthat
which indicated thatthe
theindustrial
industrial sector
sector exhibited
exhibited the the highest
highest percentage
percentage of
of fraud
fraud cases in FSs compared to other sectors in most of the years spanning from 2011 to
cases in FSs compared to other sectors in most of the years spanning from 2011 to 2021
2021 (ACFE 2012, 2014, 2016, 2018, 2020, 2022). Additionally, ACFE reports highlighted a
(ACFE 2012, 2014, 2016, 2018, 2020, 2022). Additionally, ACFE reports highlighted a rela-
relatively high number of fraud cases in Jordan during the study period. Specifically, there
tively high number of fraud cases in Jordan during the study period. Specifically, there were
were 15 fraud cases reported during the years of 2013–2017, whereas the number decreased
15 fraud cases reported during the years of 2013–2017, whereas the number decreased to 8
to 8 cases during the subsequent years of 2018–2021 (ACFE 2014, 2016, 2018, 2020, 2022).
cases during the subsequent years of 2018–2021 (ACFE 2014, 2016, 2018, 2020, 2022).
Furthermore, in 2017, the Jordan Securities Commission implemented new directives
Furthermore, in 2017, the Jordan Securities Commission implemented new directives
pertaining to corporate governance, supplanting the governance regulations manual in-
pertaining to corporate governance, supplanting the governance regulations manual intro-
troduced in 2009. Compliance with these regulations became mandatory for companies,
duced in 2009. Compliance with these regulations became mandatory for companies, neces-
necessitating the rectification of their status in accordance with the updated guidelines
sitating the rectification of their status in accordance with the updated guidelines through-
throughout 2018. Additionally, in the same year, the Jordanian government introduced
out 2018. Additionally, in the same year, the Jordanian government introduced modifica-
modifications to the income and sales tax legislation. Among the notable amendments was
tions
the to the income
progressive and sales
reduction taxincome
in the legislation. Among
tax rate the
that is notable amendments
applicable to industrialwas the pro-
enterprises
gressive reduction in the income tax rate that is applicable to industrial enterprises
over a span of five consecutive years. Specifically, the income tax rates for select companies over a
were adjusted to 15%, 16%, 17%, 18%, and 19%, respectively. Notably, the amendment is
anticipated to impact variables such as ROA and the F-score. Consequently, the timeframe
for the study sample was carefully chosen to mitigate any potential influence stemming
from alterations in governance regulations and the income tax rate on the study variables.
The introduction of the study underscored the correlation between fraud causing
financial loss and their reflection in a company’s net income. Consequently, an examination
 span of five consecutive years. Specifically, the income tax rates for select companies were
adjusted to 15%, 16%, 17%, 18%, and 19%, respectively. Notably, the amendment is antici-
pated to impact variables such as ROA and the F-score. Consequently, the timeframe for the
study sample was carefully chosen to mitigate any potential influence stemming from alter-
J. Risk Financial Manag. 2024, 17, 120 13 of 27
ations in governance regulations and the income tax rate on the study variables.
The introduction of the study underscored the correlation between fraud causing fi-
nancial loss and their reflection in a company’s net income. Consequently, an examination
of
ofthe
theoverall
overallnet
netincome
incomeofof
thethe
industrial sector
industrial spanning
sector spanningthe the
years of 2008
years to 2021
of 2008 was con-
to 2021 was
ducted. Figure 2 illustrates the findings, revealing a noticeable downturn in the
conducted. Figure 2 illustrates the findings, revealing a noticeable downturn in the indus- industrial
sector’s aggregate
trial sector’s net income
aggregate from from
net income 2012 2012
to 2017 compared
to 2017 to proceeding
compared years.years.
to proceeding While this
While
decline cannot be solely attributed to fraud, it serves as a compelling cause for exploring
this decline cannot be solely attributed to fraud, it serves as a compelling cause for explor-
this
ing specific timeframe
this specific further.
timeframe Significantly,
further. the period
Significantly, from 2012
the period to 2012
from 2017 witnessed a surge
to 2017 witnessed
in reported fraud cases, as evidenced in the ACFE report.
a surge in reported fraud cases, as evidenced in the ACFE report.

Figure2.2.Cumulative
Figure Cumulativenet
netincome
incomefor
forindustrial
industrialsector.
sector.

Therationale
The rationalefor forselecting
selectingthe theindustrial
industrialsector
sectorisisfurther
furthersubstantiated
substantiatedby byindications
indications
ofaadecline
of declineininitsitscumulative
cumulativenet netincome
income compared
compared to to other
other sectors,
sectors, particularly
particularly thethe finan-
financial
cial services
and and services
sectors.sectors. Utilizing
Utilizing cumulative
cumulative net income
net income datadata
fromfrom the ASE
the ASE website
website for
for the
three sectors
the three (services,
sectors financial,
(services, financial,andandindustrial),
industrial), thetheaverage
averagepercentage
percentagechange
changeininnetnet
income
incomeduring
duringthe theyears
yearsofof2012–2017
2012–2017 was calculated
was calculatedfor for
eacheach
sector. The The
sector. results underscore
results under-
the significance
score of the industrial
the significance sector, revealing
of the industrial averageaverage
sector, revealing percentage changes changes
percentage in cumulative
in cu-
net incomenet
mulative of income
10.73% of for10.73%
the service sector,
for the service11.06% for11.06%
sector, the financial
for thesector, −10% and
andsector,
financial for
the industrial sector. Furthermore, findings from the research conducted
−10% for the industrial sector. Furthermore, findings from the research conducted by Dor- by Dorgham et al.
(2014) revealed a deficiency in the internal control system within
gham et al. (2014) revealed a deficiency in the internal control system within industrial industrial companies
in Jordan. Additionally,
companies their study their
in Jordan. Additionally, highlighted subpar performances
study highlighted among managers
subpar performances among
in industrial
managers in firms concerning
industrial fraud prevention
firms concerning measures.measures.
fraud prevention Consequently, their findings
Consequently, their
provide
findingsfurther
provide substantiation that industrial
further substantiation that companies in Jordan may
industrial companies face heightened
in Jordan may face
susceptibility to fraudulent
heightened susceptibility to activities
fraudulent compared
activities to other sectors.
compared This
to other underscores
sectors. the
This under-
imperative of directing attention towards the industrial sector to elucidate
scores the imperative of directing attention towards the industrial sector to elucidate po- potential motives
behind fraudulent
tential motives behavior.
behind fraudulent behavior.
The
The total population comprised
total population comprised both both listed
listed and
and non-listed
non-listed (Over-The-Counter)
(Over-The-Counter) com- com-
panies,
panies, totaling 65 during the study period. The study sample consisted of
totaling 65 during the study period. The study sample consisted of 63
63 companies,
companies,
with
with22companies
companiesexcludedexcludeddue duetotothe non-publication
the non-publication of of
their annual
their annualreports. This
reports. sample
This sam-
represents 96.92% of the industrial firm’s population. Secondary
ple represents 96.92% of the industrial firm’s population. Secondary data from annual data from annual reports
published on the ASE website from 2012 to 2017 were utilized to collect FS data for the
study sample. The researchers ensured data quality and completeness by extracting infor-
mation from audited annual reports that included corporate governance disclosures. The
total annual reports for the industrial companies’ population amounted to 367, of which
349 were included in the sample, representing 95.1% of the total annual report population.
Notably, 10 annual reports were excluded from the sample due to a lack of necessary FSs
and corporate governance data for variable calculation. Throughout the data collection
J. Risk Financial Manag. 2024, 17, 120 14 of 27

process, the current study encountered limitations associated with the study sample. These
limitations are comprehensively explained in Section 6.
The collected secondary data underwent analysis utilizing the logistic regression
model within the SPSS program. This statistical model, also known as the logit model,
is commonly employed for categorization and predictive analytics. Logistic regression
utilizes a dataset of independent variables to estimate the likelihood of a specific event
occurring, such as voting or non-voting. Given that the outcome is a probability, the
independent variable typically ranges from 0 to 1.
In logistic regression, a logit transformation is applied to the odds, which represent
the likelihood of success relative to the probability of failure. This transformation is often
referred to as the natural logarithm of odds or simply the log odds.
The methodology adopted in this study is the cross-sectional approach. The principal
advantage of employing the cross-sectional approach lies in its potential for generalizabil-
ity, provided that the sample adequately represents the study population. However, a
notable disadvantage of this approach is its limitation in capturing the relationship between
variables at a single point in time.
Despite the inherent limitations of the cross-sectional approach, it was deemed suitable
for this study’s objectives, which seek to offer insights into the motives underlying fraud
during the specified study period.

3.2. Dependent Variable Measurement

The dependent variable in this study is represented by FSs being predicted to be
fraudulent. We measured this variable based on the model developed by Dechow et al.
(2011). The selection of this model was motivated by its superior predictive capabilities
for fraud compared to other models (Aghghaleh et al. 2016). Several previous studies
investigating fraud motives utilized the F-score model as a proxy for financial fraud (Devi
et al. 2021; Handoko and Tandean 2021; Hidayah and Saptarini 2019; Premananda et al.
2019; Rengganis et al. 2019; Situngkir and Triyanto 2020).
The numbers generated by the Dechow model are continuous, and the decision rule is
based on the F-score value. Therefore, an FS with an F-score value greater than (1) suggests
that the model predicts the existence of financial fraud, while an FS with an F-score value
equal to (1) or less indicates that the model predicts the absence of fraud.
For analytical purposes, dummy variables were used to measure the prediction of
fraud in FSs. If the F-score value for an FS in a particular year was equal to one or less, it
was assigned the value zero, signifying the prediction of no fraud. If the F-score value was
greater than 1, it was assigned the value (1) indicating the prediction of fraud.
To calculate the F-score as a dependent variable, the study utilized the formula for
F-score developed in the Dechow et al. (2011) study. The following formulas illustrate the
calculation of the F-score according to Dechow’s model:

Pro.
F-Score = , (1)
0.0037
where the 0.0037 is unconditional pro.

eP.V
Pro. = , (2)
1 + eP.V
where e = 2.71828183.

P.V = −7.893 + (0.790 × A) + (2.518 × B) + (1.191 × C) + (1.979 × D) + (0.171 × E)

(3)
+ (−0.932 × F) + (1.029 × G)

where
∆AA + ∆AB + ∆AC
A= (4)
Average total assets
J. Risk Financial Manag. 2024, 17, 120 15 of 27

AA = [Current Assets − Cash and Short-term Investments]

(5)
− [Current Liabilities − Debt in Current Liabilities]
AB = [Total Assets − Current Assets − Investments and Advances]
(6)
− [Total Liabilities − Current Liabilities − Long-term Debt]
AC = [Short-term Investments + Long-term Investments]
(7)
− [Long-term Debt + Debt in Current Liabilities + Preferred Stock]
∆Accounts Receivable
B= (8)
Average total assets
Inventory
C= (9)
Average total assets
[Total Assets–property, plant and equipment–Cash and Cash Equivalent]
D= (10)
Total Assets
[Salest –∆Accounts Receivablest ] − [Salest−1 –∆Accounts Receivablest−1 ]
E= (11)
[Salest−1 –∆Accounts Receivablest−1 ]
Earningst−1
   
Earningst
F= − (12)
Average total assetst Average total assetst−1
G is a dummy variable coded as 1 if the firm issued securities during year t, and if not, it is
coded as 0.

3.3. Independent Variable Measurement

The Hexagon theory categorizes fraud elements into six motives: pressure, opportu-
nity, arrogance, rationalization, competence, and collusion. This study seeks to measure
these motives through a set of independent variables and identify which of them contribute
to or mitigate fraud in the FSs that are predicted to be fraudulent. The measurements
and definitions of all independent variables that were examined in the current study are
summarized in Table 1. The logistic model employed in the study is presented below:

F-Score = α + β1 %∆TA + β2 TA_Trn + β3 LV + β4 Insi_Own + β5 ROA

+ β6 %∆Inven + β7 %Ind_AuCo + β8 LgAuCo_Age + β9 %Dir_MulPo
+ β10 ExAu_Swt + β11 CEO_Pic + β12 Dir_Chg + β13 Tone_RPTs + ε

Table 1. Independent variable measurement.

Fraud Motive Independent Variables

Independent Variable Measurement Adapted from Study
Element (and Symbols)
Change in Total Assets (Total Assets t − Total Assets t − 1) ÷ Total
(Tarjo and Sakti 2021)
(%∆TA) Assets t − 1
Asset Turnover (TA_Trn) Sales ÷ Average Total Assets (Subramanyam and Wild 2009)
(Lokanan and Satish 2018;
Sunardi and Amin 2018; Tarjo
Pressure Leverage Ratio (LV) (Total debt ÷ total assets) and Sakti 2021; Manurung and
Hadian 2013; Handoko and
Tandean 2021)
Insider Ownership Ratio Number of shares that are owned by (Tarjo and Sakti 2021; Alzoubi
(Insi_Own) management ÷ number of company shares 2016)
Return on Assets (ROA) Net Income ÷ Average Total Assets (Lokanan and Satish 2018)
J. Risk Financial Manag. 2024, 17, 120 16 of 27

Table 1. Cont.

Fraud Motive Independent Variables

Independent Variable Measurement Adapted from Study
Element (and Symbols)
Inventory Change (Inventory t − Inventory t − 1) ÷
(Premananda et al. 2019)
(%∆Inven) Inventory t − 1
Percentage of Independent Number of independent members in audit
(Bradbury et al. 2006;
Audit Committees committee ÷ Number of members in
Alzoubi 2016)
(%Ind_AuCo) audit committee
Opportunity
This variable is measured by calculating
Audit Committee Age
the natural logarithm for the average age of (Qi and Tian 2012)
(LgAuCo_Age)
audit committee members
Multiple-position
Percentages of Director Number of director positions in other (Puspitha and Yasa 2018;
Positions in Other companies ÷ total number of directors Premananda et al. 2019)
Companies (%Dir_MulPo)
Dummy variable takes number 1 if the
Auditor Switching (Sunardi and Amin 2018; Tarjo
Rationalization enterprise voluntarily changes its auditors,
(ExAu_Swt) and Sakti 2021; Devi et al. 2021)
otherwise 0.
Frequency Number of CEO The accumulated number of CEO images
Arrogance (Handoko and Tandean 2021)
Image (CEO_Pic) in the annual report of a company
Dummy variable takes number 1 if the
(Tarjo and Sakti 2021; Handoko
Change of Director directors of the firm are changed, where
Competence and Tandean 2021;
(Dir_Chg) number 0 denotes that the company’s
Devi et al. 2021)
directors have not changed.
The indicator variable is a dummy variable,
so if the company has any forms of tone,
RPTs are equal 1, otherwise equal 0. Here,
the forms of tone RPTs are as follows:
1. Loans granted to Officer, director, or
main shareholder.
2. Borrowings made by Officer, director,
or main shareholder.
3. Guarantees from Officer, director, or
main shareholder.
Tone-Related Party 4. Consulting provided by Investee,
Collusion Officer, director, or main shareholder. (Kohlbeck and Mayhew 2017)
Transactions (Tone_RPTs)
5. Legal and investment services
provided by Investee, Officer,
director, or main shareholder.
6. Unrelated business activities
performed by Investee, Officer,
director, or main shareholder.
7. Overhead reimbursement paid by
Officer, director, or main shareholder.
8. Stock transactions performed by
Officer, director, or main shareholder.

4. Results
4.1. Descriptive Statistics, Testing Outliers, Linearity, and Multicollinearity
Table 2 presents the descriptive statistics for the dependent variable (F-score) and inde-
pendent variables (%∆TA, TA_Trn, LV, Insi_Own, ROA, %∆Inven, %Ind_AuCo, LgAuCo_Age,
%Dir_MulPo, ExAu_Swt, CEO_Pic, Dir_Chg, Tone_RPTs). However, the most notable statis-
tic from Table 2 is the mean (6.88%) of the dependent variable (F-score), which means that
the number of FSs that are predicted to be fraudulent is 24.
J. Risk Financial Manag. 2024, 17, 120 17 of 27

Table 2. Descriptive statistics for dependent and independent variables.

Variable Symbol N Minimum Maximum Mean Std. Deviation

F-Score 349 0.00 1.00 0.0688 0.2534
%∆TA 349 −0.6699 1.2341 −0.0125 0.1873
TA_Trn 349 0.00 2.2087 0.5693 0.3852
LV 349 0.004 3.6603 0.4150 0.3342
Insi_Own 349 0.00 0.3535 0.0265 0.0641
ROA 349 −0.9693 0.4787 −0.0127 0.1239
%∆Inven 349 −1.00 212.2253 0.6524 11.3863
%Ind_AuCo 349 0.00 1.00 0.5521 0.3305
LgAuCo_Age 349 3.5458 4.3307 3.9835 0.1456
%Dir_MulPo 349 0. 00 1.00 0.7081 0.2693
ExAu_Swt 349 0.00 1.00 0.0602 0.2381
CEO_Pic 349 0.00 9.00 0.1432 0.8624
Dir_Chg 349 0.00 1.00 0.5215 0.5002
Tone_RPTs 349 0.00 1.00 0.3754 0.4849

Prior to conducting the logistic regression test, the assumptions associated with the
test were assessed. These assumptions include the presence of outliers, which may exert
a notable influence on the study outcomes, as well as considerations of linearity and
multicollinearity.
Upon examination, the test results revealed the identification of 9 outliers within the
sample of 349. It is important to underscore that the measurement of variables pertaining
to outliers was conducted meticulously, and the values obtained are deemed to be realistic,
as they were derived from companies’ FSs. Ultimately, it can be inferred that the presence
of outliers did not exert a significant impact on the study results.
After conducting the outlier test, the next step involved testing the linearity assump-
tion to ensure a linear relationship between the log odds of the dependent variable (F-score)
and the continuous independent variables. These variables include %∆TA, TA_Trn, LV,
Insi_Own, ROA, %∆Inven, %Ind_AuCo, LgAuCo_Age, %Dir_MulPo, and CEO_Pic. The
linearity test results indicated that all the continuous independent variables had a signif-
icance that was greater than 0.05. This implies that none of the continuous independent
variables violated the linearity principle, confirming the suitability of these variables for
the logistic regression analysis.
Before proceeding with the logistic regression analysis, a multicollinearity test was
conducted to ensure that there were no high correlations between the independent variables
in the study. Table 3 presents the variance inflation factor (VIF) for each independent
variable in the model. It is evident from the table that none of the VIF values for the
independent variables exceed 5.

Table 3. Multicollinearity test for the independent variables’ regression testing has been validated
with these results.

Variable Symbol Tolerance Variance Inflation Factor (VIF)

%∆TA 0.647 1.546
TA_Trn 0.785 1.274
LV 0.747 1.338
Insi_Own 0.852 1.173
ROA 0.549 1.822
%∆Inven 0.983 1.018
%Ind_AuCo 0.868 1.152
J. Risk Financial Manag. 2024, 17, 120 18 of 27

Table 3. Cont.

Variable Symbol Tolerance Variance Inflation Factor (VIF)

LgAuCo_Age 0.937 1.067
%Dir_MulPo 0.818 1.222
ExAu_Swt 0.894 1.118
CEO_Pic 0.927 1.079
Dir_Chg 0.917 1.091
Tone_RPTs 0.930 1.076

Furthermore, Table 4 displays the correlation matrix for the independent variables,
revealing that the correlation coefficients between the variables do not surpass 0.5. Conse-
quently, the absence of multicollinearity issues in the logistic regression model is under-
scored by Tables 3 and 4, thereby affirming the reliability of the obtained results.

Table 4. Correlation matrix for the independent variables.

Variable Symbol %∆TA TA_Trn LV ROA %∆Inven %Ind_AuCo %Dir_MulPo

%∆TA 1.000
TA_Trn −0.057 1.000
LV −0.094 0.009 1.000
ROA −0.364 −0.232 0.368 1.000
%∆Inven −0.083 −0.038 −0.015 −0.011 1.000
%Ind_AuCo −0.040 −0.119 −0.145 −0.102 −0.006 1.000
%Dir_MulPo 0.101 −0.036 −0.171 −0.095 0.017 0.098 1.000
ExAu_Swt −0.497 0.149 0.071 0.118 −0.031 −0.100 −0.020
Dir_Chg 0.031 0.288 0.017 −0.092 0.040 0.249 −0.086
CEO_Pic 0.000 0.000 0.000 0.000 0.000 0.000 0.000
Insi_Own 0.127 0.063 −0.186 −0.180 −0.003 0.034 0.332
LgAuCo_Age 0.082 0.292 0.389 −0.111 0.029 −0.056 0.090
Tone_RPTs 0.009 −0.166 −0.341 0.147 −0.002 −0.054 0.192
Variable Symbol ExAu_Swt Dir_Chg CEO_Pic Insi_Own LgAuCo_Age Tone_RPTs
ExAu_Swt 1.000
Dir_Chg −0.129 1.000
CEO_Pic 0.000 0.000 1.000
Insi_Own −0.070 0.037 0.000 1.000
LgAuCo_Age 0.181 0.215 0.000 −0.006 1.000
Tone_RPTs −0.081 −0.271 0.000 0.158 −0.407 1.000

However, it is imperative to acknowledge that while the assumptions of logistic

regression testing have been validated, these results presuppose the appropriateness of
employing this test within the context of the study to the extent of plausibility.

4.2. Measuring Goodness of Fit

The goodness of fit for the study model is assessed using the Hosmer and Lemeshow
test. The result, as shown in Table 5, indicates a significant p-value of 0.776, which is greater
than 0.05. Moreover, in Table 6, the result of the omnibus tests of the model coefficients
presents a significance of less than 5%. These tests suggest that the logistic regression model
is suitable for analysis, as it determines the ability to forecast the values of its observations
and fit.
J. Risk Financial Manag. 2024, 17, 120 19 of 27

Table 5. Hosmer and Lemeshow Test.

Chi-Square DF Sig.
4.827 8 0.776

Table 6. Omnibus tests of model coefficients.

Chi-Square DF Sig.
Step 1 Step 62.887 13 0.000
Block 62.887 13 0.000
Model 62.887 13 0.000

After assessing the goodness of fit for the study model, the feasibility of the logistic
regression model is evaluated using the −2log Likelihood test. This involves comparing
the values of −2log Likelihood at stage 0 and stage 1. A good model is indicated when the
value of the −2log Likelihood at stage 1 is lower than the value at stage 0.
In Table 7, the value of the −2log Likelihood at stage 0 is 174.807, while in Table 8, the
value of the −2log Likelihood at stage 1 is 111.920. This suggests that the logistic regression
model is not only feasible but also good.

Table 7. Iteration history.

Iteration Step −2 Log Likelihood Coefficient Constants

1 197.252 −1.725
2 176.168 −2.367
3 174.818 −2.583
4 174.807 −2.606
5 174.807 −2.606

Table 8. Model summary.

−2 Log Likelihood Cox and Snell R Square Nagelkerke R Square

111.920 0.165 0.418

In the logistic regression model, Nagelkerke R Square quantifies the extent to which
the independent variables account for the variation in the dependent variable. In Table 8,
the value of Nagelkerke R Square is 0.418, indicating that the independent variables in the
study model explain approximately 41.8% of the variation in the dependent variable (the
FSs being predicted to be fraudulent). The remaining 58.2% of the variation is attributed to
other independent variables that are not included in the study model. Table 9 presents the
frequency of expectations according to empirical data of the F-score variable (dependent
variable), and this table shows that the accuracy percentage of the model’s predictions
is 93.7%.

Table 9. Classification table.

Predicted
F-Score
Correct
Non-Fraudulent Fraudulent FSs Percentage
Observed
FSs (0) (1)
Non-fraudulent FSs (0) 322 3 99.1
F-Score
Fraudulent FSs (1) 19 5 20.8
Overall Percentage 93.7
J. Risk Financial Manag. 2024, 17, 120 20 of 27

4.3. Hypothesis Testing

We employed logistic regression analysis in SPSS to test the study hypotheses col-
lectively, aiming to identify variables or reasons that led companies to commit fraud in
their FSs, as per the Hexagon theory. The Hexagon theory categorizes fraud motives into
six elements, and we assigned independent variables in the study to assess their impact
on the dependent variable (FSs predicted to be fraudulent). The results, presented in
Table 10, indicate that only 3 out of the 13 independent variables—ROA, the percentage
of independent members in audit committees, and tone-related party transactions—had a
statistically significant impact on the dependent variable (FSs predicted to be fraudulent).

Table 10. Hypothesis test results for the variables in the logistic regression equation.

Hexagon Theory Variable Hypothesis

B S.E. Wald Sig. Exp. (B)
Elements Symbol Number
%∆TA −1.419 1.353 1.101 0.294 0.242 1
TA_Trn −0.200 0.603 0.110 0.741 0.819 2
Pressure LV 0.721 1.220 0.349 0.555 2.056 3
Insi_Own 2.015 4.261 0.224 0.636 7.501 4
ROA 14.052 3.856 13.281 0.000 * 1,266,436.55 5
%∆Inven −0.004 0.044 0.007 0.935 0.996 6
%Ind_AuCo −1.731 0.865 4.003 0.045 * 0.177 7
Opportunity
LgAuCo_Age −4.585 2.441 3.529 0.060 0.010 8
%Dir_MulPo 2.163 1.246 3.014 0.083 8.698 9
Rationalization ExAu_Swt 0.968 1.106 0.766 0.382 2.631 10
Arrogance CEO_Pic −18.294 3373.459 0.000 0.996 0.000 11
Competence Dir_Chg −1.151 0.594 3.755 0.053 0.316 12
Collusion Tone_RPTs 2.686 0.684 15.400 0.000 * 14.675 13
Constant 12.998 9.939 1.710 0.191 441,540.844
* means statistically significant.

4.3.1. Pressure Element and Predicted Fraudulent FSs

The first motive for fraud, pressure, was assessed through five independent variables:
%∆TA, TA_Trn, LV, Insi_Own, and ROA. The results presented in Table 10 reveal that the
first, second, third, and fourth hypotheses related to the pressure motive were rejected,
as their results were not statistically significant, indicating that they did not constitute a
pressure motive for committing fraud in companies’ FSs as predicted. However, the fifth
hypothesis related to the independent variable ROA was accepted. The B coefficient value
for ROA was 14.052, with a significance of 0.000, which is less than 5%, signifying that an
increase in the ROA value will lead to an increase in fraud in companies’ FSs.

4.3.2. Opportunity Element and Predicted Fraudulent FSs

Regarding opportunity, the second motive for fraud, it was measured through four
independent variables: %∆Inven, %Ind_AuCo, LgAuCo_Age, and %Dir_MulPo. The
results presented in Table 10 indicate that the sixth, eighth, and ninth hypotheses related
to the opportunity motive were rejected, as their results were not statistically significant.
This implies that these variables did not constitute an opportunity motive for committing
fraud in companies’ FSs as predicted. However, for the seventh hypothesis related to
the independent variable percentage of independent audit committees (%Ind_AuCo), the
B coefficient value amounted to −1.731, with a significance of 0.045, which is less than
5%. This result is accepted as statistically significant, indicating that any increase in the
J. Risk Financial Manag. 2024, 17, 120 21 of 27

percentage of independent members in audit committee will lead to a decrease in the

likelihood of fraud in companies’ FSs.

4.3.3. Rationalization Element and Predicted Fraudulent FSs

Regarding rationalization as the third motive for fraud, it was measured through the
independent variable ExAu_Swt. The investigation and analysis of the results in Table 10
revealed that the tenth hypothesis was not statistically significant and is rejected. The B
coefficient value amounted to 0.968, with a significance of 0.382, which is greater than 5%.
Therefore, it can be concluded that the rationalization motive had no impact on the FSs of
companies that were predicted to be exposed to fraud. This indicates that the change of
external auditor by the companies was not attributable to fraudulent motives, but rather
was due to other reasons like the expiration of the legal period in which it is allowed for
the auditor to audit the company’s accounts.

4.3.4. Arrogance Element and Predicted Fraudulent FSs

Concerning arrogance as the fourth motive for fraud, it was measured through the
independent variable CEO_Pic. The investigation and analysis of the results in Table 10
indicated that the eleventh hypothesis was not statistically significant and is rejected. The
B coefficient value amounted to −18.294 with a significance of 0.996, which is greater than
5%. Therefore, it can be concluded that the arrogance motive had no impact on the FSs of
companies that were predicted to be exposed to fraud. It can also be concluded based on
this result that the number of pictures of the CEO in the financial reports did not constitute
a fraudulent motive. This means that it is useful to search for and use other variables to
measure the motive of arrogance, which are mentioned in the conclusion section.

4.3.5. Competence Element and Predicted Fraudulent FSs

Regarding competence, the fifth motive for fraud, it was measured through the inde-
pendent variable Dir_Chg. The investigation and analysis of the results in Table 10 showed
that the twelfth hypothesis was not statistically significant and is rejected. The B coefficient
value amounted to −1.151 with a significance of 0.053, which is greater than 5%. Therefore,
it can be concluded that the competence motive had no impact on the FSs of companies
that were predicted to be exposed to fraud. Despite the average percentage of change in
directors reaching 52.15%, as shown in Table 2, this change is not imputed to fraud in FSs,
but rather may reflect aims to hire more efficient directors.

4.3.6. Collusion Element and Predicted Fraudulent FSs

Collusion, as the sixth motive for fraud, was measured through the independent
variables, particularly Tone_RPTs. The investigation and analysis of the results in Table 10
showed that the thirteenth hypothesis related to the collusion motive was statistically
significant and is accepted, with a B coefficient value of 2.686 and a significance of 0.000,
which is less than 5%. This result suggests that the existence of Tone_RPTs may indicate
the presence of fraud and manipulation in companies’ FSs.

5. Discussion
In addressing the pressure motive, this study identifies the independent variable ROA
as exerting pressure on companies with predicted fraud in their FSs. An escalation in the
ROA ratio correlates with an increased likelihood of fraud in companies’ FSs, suggesting
that managers are pressured to showcase fake success in corporate management, potentially
for financial incentives.
Regarding the relationship between the ROA and the possibility of fraud in FSs, the
research findings align with several studies, including those by Manurung and Hadian
(2013), Devi et al. (2021), Rukmana (2018), Rengganis et al. (2019), Hidayah and Sap-
tarini (2019), and Tarjo and Sakti (2021), which support a statistically significant positive
relationship. Conversely, some studies, like that by Puspitha and Yasa (2018), present
J. Risk Financial Manag. 2024, 17, 120 22 of 27

a negative existing relationship. Studies suggesting a positive relationship emphasize

managers’ attempts to demonstrate favorable results for additional incentives and investor
returns, while those indicating a negative relationship highlight managerial tendencies to
delay profit announcements to reduce the payable dividend ratios.
Concerning the motivation of opportunities, this study reveals a negative correla-
tion between the independent variable (independent members in audit committees) and
predicted fraud in FSs. An increase in the number of independent members in an audit
committee is associated with a decreased chance of fraud and manipulation in companies’
FSs. This finding aligns with Owens-Jackson et al. (2009), Rengganis et al. (2019), and
Qi and Tian (2012), indicating a negative relationship between earning management and
the independence of audit committee members, which is considered a form of fraud in
FSs. This underscores that a higher number of independent members enhances the control
effectiveness over companies’ work processes, thereby reducing the likelihood of fraud and
manipulation, a conclusion that is supported by the current study and previous research.
Regarding the third motive of collusion, this study affirms that the presence of tone-
related party transactions is positively associated with FSs being predicted to involve
fraud. This finding is consistent with the results of Kohlbeck and Mayhew’s (Kohlbeck and
Mayhew 2017) study.

6. Conclusions
In conclusion, this study, which is grounded in the Hexagon theory, endeavors to
elucidate the motivations influencing the escalation or mitigation of fraud in the FSs of
both listed and non-listed (Over-The-Counter) companies in the ASE. The Hexagon theory,
categorizing fraud motives into six distinct elements, guided the study’s investigation.
The findings underscore the impactful role of pressure, opportunity, and collusion
motives in FSs being predicted to involve fraud. Some motives were evaluated through mul-
tiple independent variables, while others were assessed through a singular variable. The
outcomes of this study provide valuable insights for FS users, fostering an enhanced under-
standing of fraud motives. This comprehension empowers users to adeptly and efficiently
evaluate the risk of fraud in FSs, contributing to a more robust and trustworthy financial
environment. This research not only contributes to the theoretical framework concerning
fraud motives but also offers particular implications for stakeholders in their pursuit of
ensuring financial transparency, integrity, and accountability in the corporate landscape.

6.1. Theoretical and Practical Implications

The findings of the current study contribute significantly to both the theoretical and
practical dimensions in the realm of fraud examination in FSs.
The theoretical contributions affirm the efficacy of employing Tone_PRTs as a proxy
to gauge the collusion variable in the Fraud Hexagon Theory. Notably, it underscores
that relevant transactions serve as indicators of potential fraud in FSs. Moreover, the
study introduces a nuanced approach by utilizing multiple independent variables to
measure certain fraud motives, recognizing the variability that has been observed in prior
research outcomes.
On a practical level, the study highlights the pivotal role of the ROA as a pressure
variable influencing FSs, with predicted fraud urging external auditors to scrutinize ROA
components diligently during corporate audits. The study prompts auditors to monitor
changes in the ROA over consecutive years, particularly focusing on indicators such as
continuous and conspicuous increases signaling potential fraud.
The study, also, raises the auditor’s attention to fraudulent methods that could manip-
ulate ROA components and underscores the importance of auditor vigilance, ensuring a
proper evaluation of revenue and expenses recognition principles and integrity in asset
valuation procedures. This includes, for example, but is not limited to, fraudulent methods
that may be used to manipulate the ROA components: recognizing the revenues early,
recording fictitious revenues, reducing the depreciation expense of an asset using incorrect
J. Risk Financial Manag. 2024, 17, 120 23 of 27

valuation methods, manipulating the timing of expenses recognition, classifying the capi-
tal expenses as operating expenses, and revaluating the assets as lower than their actual
fair value.
The findings of this study contribute to a deeper conceptual understanding of creative
accounting practices by specifically investigating accounts that are suspected to be manip-
ulated as a motive for fraud. For instance, the application of the Beford’s Law model to
the figures derived from the accounts involved in ROA computation enables more precise
identification of accounts where manipulation may have occurred.
The discussion extends to proactive measures that auditors can take including increas-
ing sample sizes, reducing acceptable risk levels for ROA components, and meticulously
reviewing Tone_RPTs accounts to allay any suspicions of fraud. Additionally, the study
recommends a strategic emphasis on governance instructions, particularly advocating for a
higher percentage of independent members in audit committees.
The negative correlation that was found between the number of independent mem-
bers and the likelihood of fraud in FSs underscores the importance of strengthening and
enforcing governance rules. This study suggests increasing the percentage of independent
members in audit committees beyond the mandated threshold, given its substantial impact
on curbing fraud in financial reports, thereby enhancing the reliability of financial markets
in developing countries.

6.2. Limitations of the Study

The current study encounters a number of limitations associated with the relatively
small number of listed and non-listed industrial companies within the study population.
Although the small size encompasses over 95% of the study population, considering the
number of firms and their annual financial reports, it remains influenced by the overall
size of the study population. Moreover, the study faces additional limitations arising from
the working conditions of certain companies or insufficient disclosures for measuring the
study variables. Specifically, some firms did not present or publish their FSs during the
study period. Additionally, during the data collection process, it became apparent that
some companies existed for a duration that was shorter than the study period. However,
the total number of companies lasting less than 6 years is relatively low, not exceeding
5%. These companies are included in the sample, given that each FS is independent, as
evidenced by variations in companies’ business results from year to year.
Despite the small sample size, it is arguable that the results of this study can be
generalized to both the listed and non-listed industrial sector on the ASE. The sample
covers a significant percentage of the study population during the study period that
extended from 2012 to 2017.
The study drew upon financial data extracted from the FSs of industrial companies to
compute the variables. However, it is important to acknowledge that FSs in the accounting
domain are subject to certain limitations. These limitations include the use of personal judg-
ment in calculating certain items and the reliance on a historical cost basis for measuring
items. Consequently, these inherent limitations in FSs may at times lead to discrepancies
between the reported figures and the actual financial standing of the company.

6.3. Future Research

The findings of the current study present an avenue for future research to explore
potential changes in motives driving fraud, particularly investigating fraud motives in
different periods of time, especially after 2018. Moreover, the study results can provide
valuable insights for researchers and stakeholders assessing the effectiveness of governance
rule amendments that companies are mandated to adhere to from the outset of 2018.
Combating fraud in companies’ FSs is crucial due to the significant harm that it inflicts
on FS users, including investors and lenders, as well as the broader national economy.
The current study focused on investigating fraud motives in the industrial sector for the
period of 2012–2017, as this sector exhibited a higher prevalence of fraud cases compared
J. Risk Financial Manag. 2024, 17, 120 24 of 27

to other sectors such as services and finance. Therefore, future researchers are encouraged
to delve into fraud motives across all sectors to foster a deeper and more comprehensive
understanding of the fraud phenomenon. Such a comprehensive understanding is crucial
for refining methods and regulations that are aimed at combating fraud.
Most previous studies used the number of photos of the CEO in financial reports, and
this variable alone may not be sufficient to express the motive of arrogance. Therefore, this
study suggests using other variables to measure this motivation in a way that enhances the
possibility of describing this motivation. For example, the company’s employee turnover
rate can be used to measure management arrogance, and the proposed variable can be
justified by the fact that the arrogant CEO damages his working relationship with lower
management and employees, which prompts them to find other job opportunities. Also, a
decrease in the number of training courses and seminars that are held to train the company’s
middle management and employees may be an indication of the arrogance of management
that does not care about developing the skills of their employees.
It is, also, vital to conduct continuous studies on fraud motives over varying time
intervals to grasp the evolving nature of these motives over time. This ongoing exploration
ensures that decision-makers are equipped with updated insights into the motives driving
fraud, empowering them with proactive measures to effectively combat fraud.

Author Contributions: Conceptualization, A.A.B.; Data curation, A.A.B. and B.K.A.; Formal analysis,
A.A.B. and S.R.S.W.; Investigation, A.A.B. and Y.A.A.H.; Methodology, A.A.B., Y.A.A.H., S.R.S.W.
and B.K.A.; Project administration, A.A.B.; Validation, A.A.B., S.R.S.W. and B.K.A.; Visualization,
A.A.B. and Y.A.A.H.; Writing—original draft, A.A.B., Y.A.A.H., S.R.S.W. and B.K.A.; Writing—review
and editing, A.A.B., Y.A.A.H., S.R.S.W. and B.K.A. All authors have read and agreed to the published
version of the manuscript.
Funding: This research received no external funding.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: The collected and analyzed data are available on the Amman Stock
Exchange website at https://www.ase.com.jo/en/history?history_category=64 (accessed on 1 March
2022). Additionally, some of the data collected from the financial reporting disclosures are pub-
lished on the Amman Stock Exchange website at https://www.ase.com.jo/en/disclosures?symbol=
&category_id=1&published[min]=&published[max]= (accessed on 22 May 2022). Moreover, some of
the data are collected from the Jordan securities commission website at https://www.jsc.gov.jo/JSC_
financial_Reports.aspx (accessed on 8 October 2023).
Conflicts of Interest: The authors declare no conflicts of interest.

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