Using the Balanced Scorecard Scale in Building, a Four-Track

Measurement Model to Predict the Intellectual Capital of Industrial

Stockholder Companies Listed on the Amman Stock Exchange for the

Period (2016–2020)

LAITH AKRAM AL-QUDAH

Department of Accounting and Accounting Information System, Amman University College,

Al-Balqa Applied University,

Amman, 11937,

JORDAN

MOHAMMAD MAHMOUD HUMEEDAT

Department of Accounting and Accounting Information System, Amman University College,

Al-Balqa Applied University,

Amman, 11937,

JORDAN

KHAWLA KASSED ABDO

Department of Finance and Banking, Faculty of Business,

Al-Blaqa Applied University,

Amman, 11937

JORDAN

HANAN AHMAD QUDAH

Department of Financial and Administrative Sciences, Ajloun University College,

Al-Balqa Applied University,

Amman, 11937,

JORDAN

EMILIO MARTÍN

Accounting and Finance Department, Faculty of Economics and Business,

University of Zaragoza,

Zaragoza, 50005,

SPAIN

Abstract: - The goal of this study was to investigate the use of the balanced scorecard scale in the development

of a four-track measuring model to estimate the intellectual capital of industrial joint stock businesses listed on

the Amman Stock Exchange. The sample for this study is made up of 59 industrial public joint stock businesses

registered on the Amman Stock Exchange (ASE) between 2016 and 2020. A multiple linear regression analysis

using EVIEWS software and the findings suggest that the balanced scorecard has a favourable influence on

intellectual capital from the financial, customer, internal-business-process, learning, and growth perspectives.

According to the study, make suggestions based on the results of our inquiry to increase the intellectual capital

of these companies. This might involve revising the company's human capital management methods,

strengthening customer relationships, or concentrating more on innovation and learning. The current study is

the first of its kind to be conducted in a developing nation, such as Jordan, and the findings might be useful to

other underdeveloped nations.

Key-Words: - Balanced Scorecard scale, four-track measurement, intellectual capital, industrial sector, Amman

Stock Exchange.

Received: November 21, 2022. Revised: March 19, 2023. Accepted: April 9, 2023. Published: April 24, 2023.

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Laith Akram Al-Qudah,

Mohammad Mahmoud Humeedat,

Khawla Kassed Abdo,

Hanan Ahmad Qudah, Emilio Martín

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1 Introduction

Intellectual capital, sometimes known as an

organization's "hidden worth," refers to the

intangible assets that contribute to its success, such

as information, expertise, skills, and inventive ideas.

These assets might be difficult to define, but they

are essential for gaining a long-term competitive

edge, [25]. Measuring and maintaining intellectual

capital is so critical for firms seeking to improve

performance and sustain development. The

Balanced Scorecard is one instrument that has been

frequently utilized for this purpose. Furthermore, the

Balanced Scorecard is a management tool that

assists firms in aligning their company operations

with their vision and strategy. This is accomplished

by monitoring performance from four perspectives:

financial, customer, internal processes, and learning

and growth, [14].

The balanced scorecard was created as a

performance measurement framework that

combined strategic non-financial performance

measures with traditional financial metrics to

provide managers and executives with a more

complete picture of organizational performance. Its

initial application as a simple performance

measurement framework has evolved into a full

strategic planning and management system, [32].

The balanced scorecard application entails

strategizing the use of available resources, such as

the human workforce, finances, and other resources

to achieve set goals. Profitability is an important

link in maximizing an organization's wealth; it is

critical because it is the measure of performance in

the production of goods or services and the means

by which the firm's future is ensured. Long-term

financial results and shareholder wealth are

expected to improve because of operational

improvements, [34].

To apply the Balanced Scorecard scale in

developing a four-track assessment model to

forecast intellectual capital, define important

indicators and goals relevant to an organization's

intellectual capital within each of these four

perspectives. Metrics such as working knowledge

and skills, the success of training and development

programs, the effectiveness of innovation and R&D

activities, and the extent to which intellectual

property is safeguarded and utilized might be

included, [13]. Once the essential metrics and

objectives have been determined, a system for

tracking and assessing these metrics on a regular

basis must be developed. This may entail gathering

information from a variety of sources, including

staff surveys, consumer feedback, financial records,

and other sources. This data would then need to be

analyzed to establish how well the company is

performing in each of the four perspectives, and this

information would be used to identify areas for

improvement and set new objectives for future

performance, [12], [35].

The novelty of the concept of intellectual

capital, as well as the increased interest in it in the

modern era, the fact that it does not appear

separately or as a value in enterprise financial

statements, and the difficulty of disclosing it and

indicating its impact on companies, all make this a

topic worthy of research, as it has value but is

difficult to determine. Therefore, this study seeks to

know the relationship between the application of the

balanced scorecard from its four perspectives

(financial perspective, customer perspective,

learning and growth perspective, and the perspective

of the internal business process) and the prediction

of intellectual capital as a point from which

intellectual capital can be measured by conducting

an applied study on industrial stockholder

companies listed on the Amman Stock Exchange.

Thus, the problem of the study is to answer the

following main question: "Is there an impact of the

application of the balanced scorecard on the

prediction of intellectual capital in industrial

stockholder companies listed on the Amman Stock

Exchange?"

This study’s technical contribution is to improve

decision-making. Organizations may make better-

informed decisions about how to allocate resources

and prioritize activities to optimize the value of their

intellectual capital by tracking and measuring key

indicators connected to intellectual capital.

Furthermore, through defining goals and analyzing

success in each of the Balanced Scorecard's four

perspectives, firms may discover areas for

improvement and take action to increase employee

knowledge and skills, customer happiness, internal

procedures, and innovation. Likewise, firms may

improve their competitiveness by better

understanding and managing their intellectual

capital and exploiting their unique expertise and

talents to produce value for customers and other

stakeholders. Ultimately, companies may

demonstrate responsibility to stakeholders and

increase transparency around their operations and

performance by measuring and reporting on their

intellectual capital performance on a regular basis.

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Mohammad Mahmoud Humeedat,

Khawla Kassed Abdo,

Hanan Ahmad Qudah, Emilio Martín

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The relationship between both intellectual capital

and the balanced performance measure is

interrelated and very important, as it is necessary to

align intangible assets with the business

organization strategy, and it is also important to

focus on the learning and growth processes factor

for the balanced performance measure since human

resources are the main reason that drives the other

factors for the balanced performance measure. In

addition, the market value of the organization stems

from its intangible assets, which are necessary for

its continuation, [30]. This study intends to use the

balanced scorecard performance measure to

construct a four-track measurement model to predict

intellectual capital in industrial joint stock

companies listed on the Amman Stock Exchange, as

well as to identify the concept of intellectual capital

and the models used to measure it by employing a

variety of financial tools that are compatible with

the available data and information announced by

industrial joint stock companies listed on the

Amman Stock Exchange.

The paper is then structured in four sections:

Section 2 is devoted to reviewing literature; Section

3 tackles the theoretical framework and hypotheses;

Section 4 outlines the research methodology; and

finally, the conclusion and recommendations are

given in Section 5.

2 Literature Review

2.1 Balanced Scorecard Scale

The Balanced Scorecard (BSC) is a strategic

planning and management approach for aligning

business operations with an organization's vision

and strategy, improving internal and external

communication, and tracking organizational

performance against strategic goals, [19]. The

Company Strategy Canvas (BSC) is a framework

that considers four viewpoints: financial, customer,

internal business processes, and learning and

growth, [8]. These four perspectives are used to

create a set of measurements and goals for assessing

the organization's progress. The Balanced Scorecard

scale denotes the extent to which it is used inside a

firm. It may be used at several levels, from strategic

to operational. The Balanced Scorecard is used at

the strategic level to translate an organization's

vision and strategy into a set of defined, measurable,

achievable, relevant, and time-bound (SMART)

objectives and activities. These objectives and

initiatives are subsequently passed down to lower

levels of the organization, where they may be used

to guide and monitor team and individual

performance, [15], [24].

Each of the Balanced Scorecard's four

perspectives is measured using a set of key

performance indicators (KPIs). The particular KPIs

that will be utilized will be established by the

organization's goals and objectives, as well as the

industry and market in which it operates. Financial

measurements like sales, profit, return on

investment, and cash flow is examples of common

KPIs in finance. Customer KPIs might include

indications of customer enjoyment, loyalty, and

retention. Internal process KPIs might include

efficiency, productivity, and quality metrics.

Employee engagement, training, and development

indicators may be included in learning and growth

KPIs, [21], [36]. Organizations may acquire an

understanding of how well they are performing in

each of the four perspectives by tracking and

measuring these KPIs over time and making

adjustments as needed to stay on track and meet

their objectives. It is critical to remember that the

Balanced Scorecard is not a one-size-fits-all solution

and that organizations should customize their

scorecard to meet their specific requirements and

goals. It is also vital to analyze and update the KPIs

being measured on a regular basis to ensure that

they remain relevant and linked to the firm's overall

strategy, [16], [45].

In the Balanced Scorecard, performance is

measured using no specific scale. Organizations, on

the other hand, often define their own objectives and

standards for each of the indicators included in their

Balanced Scorecard, [44]. These targets and

benchmarks should be based on the organization's

unique goals and objectives while being consistent

with the organization's overall strategy. If a

company uses the Balanced Scorecard to track its

financial performance, it can set goals for revenue

growth, profitability, and cash flow. If it uses the

Balanced Scorecard to measure customer

performance, it may set goals for customer

satisfaction, loyalty, and retention. Organizations

often begin by establishing their vision and strategy,

as well as determining the primary goals and

objectives that they want to achieve, before

developing a four-track measuring model based on,

[37]. They can next choose the important indicators

that they wish to track in each of the Balanced

Scorecard's four viewpoints. After identifying the

indicators, companies may create objectives and

benchmarks for each indication and measure

progress toward these targets over time. It is critical

to evaluate and update the indicators in the Balanced

Scorecard on a regular basis to ensure that they are

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Mohammad Mahmoud Humeedat,

Khawla Kassed Abdo,

Hanan Ahmad Qudah, Emilio Martín

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successfully assessing progress toward the

organization's goals, [42].

Intellectual capital refers to the intangible assets

of a company, such as its knowledge, skills, and

experience. It is often considered to be a key source

of competitive advantage and can take many forms,

including patents, trademarks, copyrights, and

proprietary technology. Intellectual capital is also

closely tied to a company's human capital, or the

knowledge and expertise of its employees.

Intellectual capital can be a difficult concept to

measure and quantify, as it is not a tangible asset

like property or equipment. However, companies

may seek to manage and leverage their intellectual

capital in order to generate economic value and

improve their overall performance. This can be done

through activities such as investing in employee

training and development, building strong

partnerships and networks, and protecting

intellectual property through patents and other legal

means, [16], [45].

2.2 Intellectual Capital

Human capital, structural capital, and social capital

are the three major kinds of intellectual capital.

Human capital refers to a company's employees'

knowledge, abilities, and experience. Education and

training, as well as on-the-job experience and skill,

are examples of this. Human capital is frequently

regarded as a crucial engine of innovation and

productivity, and businesses may spend on training

and development programs to create and retain

highly trained staff, [10]. The methods, procedures,

and infrastructure that a corporation has in place to

develop, manage, and use its intellectual capital are

referred to as structural capital. Intellectual property

rights, research and development projects, and data

management systems are examples of this. Social

capital refers to a company's ties and networks with

external stakeholders such as customers, suppliers,

and other partners, [38]. These connections may be

valuable sources of information, knowledge, and

other resources that can help a business innovate

and expand. Managing and exploiting intellectual

capital may be a difficult process since it requires

managing, not just real assets like property and

equipment, but also intangible assets such as

knowledge and experience. However, it may also be

a significant source of competitive advantage,

assisting businesses to innovate, expand, and

outcompete their competitors, [22].

Intellectual capital would include examining

and assessing the topic's existing body of

knowledge. Examining scholarly research papers,

books, and other sources to gain a better

understanding of intellectual capital and its different

components, such as human capital, structural

capital, and social capital, [17]. When reviewing the

literature on intellectual capital, researchers may

want to look at how different writers and researchers

have defined and conceptualized the term, as well as

how it has been assessed and evaluated. Individuals

might also look at how intellectual capital has been

connected to other outcomes like innovation,

productivity, and performance. Furthermore,

individuals may investigate the numerous

methodologies and frameworks that have been

established for managing and utilizing intellectual

capital, including training and development

programs, intellectual property protection, and R&D

funding, [5], [41].

There have been numerous techniques for

measuring intellectual capital, including financial

metrics; some studies have utilized financial

indicators to examine the influence of intellectual

capital on a company's success, such as return on

investment or market value. Other studies have

utilized non-financial criteria to quantify the

quantity of intellectual capital within a firm, such as

the number of patents filed or the number of staff

training programs, [26]. Balanced scorecards, which

are performance evaluation systems that track both

financial and non-financial metrics, have been used

by some academics to examine the influence of

intellectual capital on a company's overall success,

[20]. It is crucial to emphasize that there is no single

"right" technique to evaluate intellectual capital, and

different approaches may be more or less

appropriate depending on the study's unique context

and objectives. A literature study on intellectual

capital could look at how it might be managed and

used to improve performance and produce value, in

addition to the many assessment methodologies.

This might involve methods like investing in staff

training and development, preserving intellectual

property, forming strong relationships and networks,

and investing in R&D, [18], [39].

2.3 Hypotheses Development

Despite the extreme importance of the relationship

between both intellectual capital and the balanced

performance measure, few studies have addressed

how to integrate them. According to [35], the

balanced scorecard is a management model (not just

a measurement tool) that enables companies to

define their strategy and vision and translate them

into particular actions controlled by a coherent set of

action performance measures. It provides responses

across internal business processes and external

results, advancing strategic performance and results

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Mohammad Mahmoud Humeedat,

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Hanan Ahmad Qudah, Emilio Martín

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indefinitely. A balanced scorecard is used by

organizations all over the world to translate strategy

and vision into measurable goals.

Nonetheless, [10] argue that a balanced

scorecard is advantageous because it combines a

business organization's direction, foundation, and

sight to create organizational performance measures

that combine the old with the new while converting

long-term objectives and strategies (such as

satisfying customers) into tangible actions that can

be taken internal or external. Some clever managers

organized, communicated, and managed their plans

using the balanced scorecard approach, which

places a larger focus on strategy than on control.

The balanced scorecard has progressed from an

improved measuring system to a central

management system. Furthermore, [40] argued that

the measuring performance system encompasses all

organizational tasks, referring to the financial

perspective because it relates to corporate finance

and accounting from the customer perspective since

it relates to marketing, the internal-business-process

perspective, and the value addition world in general,

and the perspectives of learning and development

for staff members and human capital.

On the other hand, the study conducted by [3]

concluded that the balanced scorecard could provide

managers with the advantages they need to

accurately assess themselves, thus improving their

competitiveness. Businesses' primary goal is to

develop overall performance and profit. When

managers use the balanced scorecard as a

performance measurement tool, they can achieve

this goal. Despite the limitations identified by some

researchers, the balanced scorecard is beneficial

when implemented by organizations because it

incorporates both financial and non-financial

variables in measuring performance at any given

time. Organizations should use the balanced

scorecard model as a performance measurement tool

because it provides the most benefits.

Despite its effectiveness and broad adoption in

many businesses, the balanced scorecard, like other

assessment methods, has attracted criticism from a

number of sources. Academics made up the vast

majority of these complaints. According to [4], one

of the balanced scorecard's shortcomings is that the

causation linkages between the areas of

measurement in the balanced scorecard are overly

basic and unidirectional. Some authors have pointed

out that a few of the proposed components of

assessment in the balanced scorecard do not have a

causation link, citing the connection between client

loyalty and financial success as an illustration of

these limits. Nonetheless, [7] demonstrated that the

balanced scorecard disregards the sequence. This

critical point of the balanced scorecard assumes that

the relationship between different points in time

must be interpreted. In this view, a balanced

scorecard does not explain the role of time in its

cause-and-effect relationships. A balanced scorecard

does not include time in cause-and-effect

relationships, nor does it separate cause-and-effect

relationships in time. Moreover, the traditional

balanced scorecard concept is ineffective for

enhancing corporate sustainability according to [43].

Likewise, [11] studied the relationship between

intellectual capital and competitive advantage. They

found that intellectual capital has been defined as a

collection of intangibles (capabilities, competencies,

and resources) that elevate organizational

performance and value creation. This suggests that

there are causal relationships between intellectual

capital and the creation of organizational value.

Furthermore, this system allows for an in-depth

analysis of a company's performance (from the

standpoint of intellectual capital) in order to identify

possible opportunities for enhancing

competitiveness. Nevertheless, unfortunately, many

organizations focus on stocks or resources primarily

or exclusively because they are relatively easy to

measure.

Besides, [28] have explained the importance of

intellectual capital by comparing it to technological

advances. It is regarded as one of the intangible

assets that have replaced machines and natural

resources, as well as one of the most valuable

factors in a company's financial performance

growth. Intellectual capital is the difference between

a company's market value and book value. It

constitutes an intangible asset through which

creative ideas and the necessary knowledge stock

can be enhanced to promote companies, improve

their overall performance, increase their market

share, and increase their competitiveness. In

addition, [31] agree that intellectual capital is a

critical component in achieving organizational

performance. A process of changing the capital

structure is underway in order to establish a

substantial share of essential intangible resources.

As a result, these intangible resources (the capacity

to use knowledge and workplace structure) play a

role in increasing the company's financial capability

and contributing to the production of valuable

resources inside the business. Furthermore, [29]

agree with the specifically carried and add that

knowledge management is a company's capacity to

capitalize on chances to boost competitiveness and

increased investments. Multilevel assessment, which

combines individual and collective knowledge and

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Hanan Ahmad Qudah, Emilio Martín

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abilities with institutional and collaboration

processes, is widely used in this vantage point.

Furthermore, [9] demonstrated that brains and

information stock are transformed into innovation

when the power of academic freedom generates

specific financial rewards for businesses through

properly coordinating and appropriate investment

for intangible resources. These perspectives and

underlying principles are a wonderful place to start

while learning about intellectual capital.

The balanced scorecard, with its own structure,

contributes to the formation and operation of

companies of innovation, according to [33].

Moreover, [23] discovered that the balanced

scorecard's educational perspective, period to make,

and customer capital are factors in the growth of

intellectual capital's technological capability and

social resources; the balanced scorecard's company

internal perspective, process capital; and the

balanced scorecard's customer, customer assets.

According to [27], the common technique of

Taiwanese intellectual capital firms of enhancing

quality, internal-business-process, and studying

perspectives there at expense of quick financial

results helped contribute to intellectual wealth

creation and, thus, long-term competitive nature.

This study is based on the following

assumptions, which are influenced by the

investigation of past and conceptual studies linked

to the basis of this study and are based on the study

problem and its goals:

H01: There is no statistically significant effect of

using the balanced scorecard scale in building a

four-track measurement model to predict the

intellectual capital of industrial stockholder

companies listed on the Amman Stock Exchange.

The main hypothesis has the following sub-

hypotheses:

H01-1: There is no statistically significant effect of

applying the financial perspective of the balanced

scorecard to predict intellectual capital in industrial

stockholder companies listed on the Amman Stock

Exchange.

H01-2: There is no statistically significant effect of

applying the customer perspective of the balanced

scorecard to predict intellectual capital in industrial

stockholder companies listed on the Amman Stock

Exchange.

H01-3: There is no statistically significant effect of

applying the internal-business-process perspective

of the balanced scorecard to predict intellectual

capital in industrial stockholder companies listed on

the Amman Stock Exchange.

H01-4: There is no statistically significant effect of

applying the learning and growth perspective of the

balanced scorecard to predict the capital in

industrial stockholder companies listed on the

Amman Stock Exchange.

3 Study Methodology

The study community consists of all Jordanian

industrial public joint stock companies listed on the

Amman Stock Exchange, as there are (77)

companies according to the 2014 company guide

published on the website of the Amman Stock

Exchange (www.exchange.jo). The sample of the

study included all Jordanian industrial public joint

stock companies listed on the Amman Stock

Exchange from 2016–2020, with the exception of

the following companies: 1. companies that were

merged or liquidated during the study period. 2.

Companies that did not publish their financial

statements regularly during the study period.

Accordingly, the final sample size that met the

previous conditions is equal to (59) companies,

which constitute 76% of the community's size.

While the dependent variable, intellectual

capital, is the method of the ratio of market value to

book value that will be used to measure intellectual

capital. On the other hand, independent variables,

the balanced scorecard is measured as shown in

Table 1.

Table 1. Independent variables

From the

financial

perspective,

From the

customer

perspective,

From the

internal-

business-

process

perspective,

The learning

and growth

perspective,

Earnings per

share (EPS)

The ratio of

marketing

expenses to

sales

The proportion

of research and

development

costs to sales

The ratio of

training

expenses to

sales

Rate of return

on assets

(ROA)

Sales growth

Asset turnover

rate

The percentage

of sales

allocated to

employees

The rate of

return on

equity (ROE)

Market share

The ratio of

salaries to

expenses

3.1 Methods for Measuring Study

Hypotheses

Measurement of the first main hypothesis: There is

no statistically significant effect of using the

balanced scorecard scale in building a four-track

measurement model to predict the intellectual

capital of industrial stockholder companies listed on

the Amman Stock Exchange. The basic model of

this study is represented by the following equation:

IC= α + (β1X1) + (β2X2) + (β3X3) + (β4X4) + e

Whereas:

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Hanan Ahmad Qudah, Emilio Martín

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IC: Intellectual Capital

α: Constant of the regression relationship

X1: The financial perspective of the balanced

scorecard

X2: The customer perspective of the balanced

scorecard

X3: The internal-business-process perspective of the

balanced scorecard

X4: The learning and growth perspective of the

balanced scorecard

UIT: The number of random changes that the model

does not explain

β: Regression coefficients for independent variables

e: Random error

However, the model proposed, developed, and used

in this study is:

IC = α + (β1 EPS) + (β2 ROA) + (β3 ROE) + (β4

GRev) + (β5 MktSh) + (β6 RD) + (β7 AT) + (β8

TrExp) + (β9 SaExp) + e

Whereas:

α: Constant of the regression relationship

β: Regression coefficients for independent variables

EPS: Earnings per share

ROA: Return on asset

ROE: Return on equity

GRev: Sales growth

MktSh: Market share

RD: Research and Development Costs to Sales

AT: Asset turnover rate

TrExp: The ratio of training expenses to sales

SaExp: The ratio of salaries to expenses

e: Random error

Measurement of the first sub-hypothesis: There is

no statistically significant effect of applying the

financial perspective of the balanced scorecard to

predict intellectual capital in industrial stockholder

companies listed on the Amman Stock Exchange.

This hypothesis includes the financial perspective of

the balanced scorecard in order to measure

performance indicators based on the financial

perspective. The following indicators will be used:

earnings per share (EPS), rate of return on assets

(ROA), and rate of return on equity (ROE).

To test this hypothesis, the following model will be

constructed:

IC = α + (β1 EPS) + (β2 ROA) + (β3 ROE) + e

Measurement of the second sub-hypothesis: There is

no statistically significant effect of applying the

customer perspective of the balanced scorecard to

predict intellectual capital in industrial stockholder

companies listed on the Amman Stock Exchange.

This hypothesis includes the customer perspective

of the balanced scorecard. The following indicators

will be used to measure the performance indicators

based on the customer perspective: The ratio of

marketing expenses to sales, sales growth, and

market share

To test this hypothesis, the following model will be

constructed:

IC = α + (β1 GRev) + (β2 MktSh) + e

Measurement of the third sub-hypothesis: There is

no statistically significant effect of applying the

internal-business-process perspective of the

balanced scorecard to predict intellectual capital in

industrial stockholder companies listed on the

Amman Stock Exchange. This hypothesis includes

the internal-business-process perspective of the

balanced scorecard. To measure the performance

indicators based on the internal business process

perspective, the following indicators will be used:

the ratio of research and development costs to sales

and the asset turnover rate.

To test this hypothesis, the following model will be

constructed:

IC = α + (β1 RD) + (β2 AT) + e

Measurement of the third sub-hypothesis: There is

no statistically significant effect of applying the

learning and growth perspective of the balanced

scorecard to predict the capital in industrial

stockholder companies listed on the Amman Stock

Exchange. This hypothesis includes the learning and

growth perspective of the balanced scorecard. To

measure performance indicators based on the

learning and growth perspective, the following

indicators will be used: The ratio of training

expenses to sales, the percentage of sales allocated

to employees, and the ratio of salaries to expenses.

To test this hypothesis, the following model will be

constructed:

IC = α + (β1 TrExp) + (β2 SaExp) + e

4 Findings and Discussion

Testing the validity of data for statistical analysis:

The models of this study belong to the general linear

model (GLM), which requires the availability of

many conditions before its application; therefore,

the data of this study should be examined to verify

their compliance with the conditions of the (GLM).

On the contrary, a false correlation arises between

the independent and dependent variables of the

study, and therefore the correlation loses its ability

to explain or predict the phenomenon in question.

Therefore, before starting to find regression

equations and do data analysis, these data should be

examined to verify that they are free of statistical

problems that may negatively affect the results of

testing the study hypotheses. To ensure linearity

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DOI: 10.37394/23207.2023.20.82

Laith Akram Al-Qudah,

Mohammad Mahmoud Humeedat,

Khawla Kassed Abdo,

Hanan Ahmad Qudah, Emilio Martín

E-ISSN: 2224-2899

893

Volume 20, 2023

assumption, independence assumption, and normal

distribution assumption, which contribute to the

selection of appropriate statistical methods for

testing hypotheses within the selected sample.

Table 2. Shows the Anderson-Darling test of the

normal distribution

P-value

Anderson-Darling

Variable

1.334

Intellectual capital

4.55

Earnings Per Share

0.76

Return on Assets

0.5656

Return on Equity

0.7

Sales growth

0.5743

Market share

0.7729

Research and

Development Costs

to Sales

0.7122

Asset turnover rate

0.3111

The ratio of training

expenses to sales

0.211

The ratio of salaries

to expenses

The values of observations must follow the normal

distributions to be valid for the general linear model

(GLM), and if this criterion is not satisfied, the

information is processed with the arithmetic mean

or its sum of squares. It has been verified that the

private data follows a normal distribution based on

the Anderson-Darling test, and the decision rule is

to accept the nihilistic hypothesis (H0): the data is

normally distributed) if the probability of the

Anderson-Darling test is greater than (0.05).

From Table 2, we note that the probability of

the Anderson-Darling parameter test is greater than

0.05, which indicates that all study variables follow

the normal distribution

The generalized linear model (GLM) is

predicated on the premise of variable independence,

and if this is not satisfied, the model suffers from

multi-collinearity. To address this issue, a

parameterization procedure is performed, in which

the Collinearity Diagnosis scale is used to compute

the variance inflation factor (VIF) from among

study variables. In [19], author demonstrated that a

VIF score greater than 10 signifies the presence of

an issue with a linear plurality of the independent

variable. According to [19], the value of the

variance inflation coefficient in Table 2 was larger

than 1 and less than 10, indicating that the research

models are free of the problems of linear

interference.

In this part of the study, we discuss the

description of the study variables after the validity

of the data has been verified to test the hypotheses,

and the process of describing the variables is based

on the use of statistical measures that clarify the

most important main characteristics of the

dependent and independent study variables. The

following is a presentation of the measures used to

describe the variables of the study.

Table 3. Descriptive statistics of Intellectual capital

Measurement

Intellectual capital

Means

27.736

Maximum value

87.967

Minimum value

7.6565

Standard Deviation

34.342

The average intellectual capital amounted to 27.736,

with a standard deviation of 34.342. While the

highest value recorded during the period was

(87.967), the lowest value was (7.6565) as shown in

Table 3. This apparent disparity in intellectual

capital efficiency may be due to the industrial

companies' differing awareness of the importance of

intellectual assets and their contribution to adding

value to the institution, which was reflected in the

policies followed in generating revenue through

intellectual value added to its operations. Table 4

shows the values of the descriptive statistical

measures of the independent variables of the

study.The average return on assets amounted to

(0.0075), with a standard deviation of (0.0290).

While the highest value recorded during the period

was (0.032), the lowest value was (-0.0372). The

decrease in this percentage may be attributed to the

large size of the investment industrial companies'

assets, which exceeded $6 billion in 2020. The

relatively high standard deviation value compared to

the arithmetic mean also indicates a discrepancy in

the industrial companies to exploit its assets, and

this is confirmed by the maximum return on assets,

which amounted to (0.0315) for the year 2013. On

the other hand, the average return on equity

amounted to (0.2272), with a standard deviation of

(0.3015). While the highest value recorded during

the period was (0.5286), the lowest value was (-

0.1166). Here, the value of the standard deviation

that exceeded the value of the arithmetic means

indicates a clear discrepancy in this ratio, and this is

due to the industrial company's exposure to several

losses during the said period, but it recovered from

these losses and made profits again, as the

maximum value of the return on income.

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Laith Akram Al-Qudah,

Mohammad Mahmoud Humeedat,

Khawla Kassed Abdo,

Hanan Ahmad Qudah, Emilio Martín

E-ISSN: 2224-2899

894

Volume 20, 2023

Table 4. Descriptive statistics of independent

variables

Measurement

Means

Maximum

value

Minimum

value

Standard

Deviation

Return on

Assets

0.0075

0.0315

-0.037

0.029

Return on

Equity

0.2272

0.5286

-0.117

0.3015

The ratio of

marketing

expenses to

sales

0.0201

0.0367

0.0133

0.0096

Sales growth

0.5727

2.5122

-0.186

1.0972

Research and

development

costs to sales

0.0039

0.0049

0.002

0.0012

Asset turnover

rate

0.052

0.0613

0.0347

0.0105

The ratio of

training

expenses to

sales

0.0008

0.0011

0.0004

0.0003

The ratio of

salaries to

expenses

0.246

0.3502

0.0336

0.1331

The average value of marketing expenses to sales

was (0.0201), with a standard deviation of (0.0096).

While the highest value recorded during the period

was (0.0367), the lowest value was (0.0133). This

indicates that the industrial companies followed a

consistent marketing policy during the same period.

While, the average sales growth values amounted to

0.5727, with a standard deviation of 1.0972. While

the highest value recorded during the period was

(2.5122), the lowest value was (-0.1860).

This is a sign of the significant variation in the

industrial companies’ ability to generate income

during the period, and this was clearly reflected in

the returns. In addition, the average ratio of research

and development costs to sales was (0.0039), with a

standard deviation of (0.0012). While the highest

value recorded during the period was (0.0049), the

lowest value was (0.0020). This is a reference to the

industrial companies' consistent policy of supporting

research and development. The average value of the

asset turnover rate was (0.0520), with a standard

deviation of (0.0105). While the highest value

recorded during the period was (0.0613), the lowest

value was (0.0347). This indicates that industrial

companies maintain their operational performance

by exploiting available assets and resources to

achieve revenue.

Besides, the average value of training expenses

to revenues was (0.0008), with a standard deviation

of (0.0003). While the highest value recorded during

the period was (0.0011) and the lowest value was

(0.0004). This is an indication of the stability of the

training plans required by the industrial companies,

and the significant decrease in training expenses

compared to revenues may be attributed to the

relative stability of revenue sources as well as to the

increased dependence of the industrial companies on

the use of consultants to make decisions. Moreover,

the average salary-to-expense ratio was (0.2460),

with a standard deviation of (0.1331). While the

highest value recorded during the period was

0.3502, the lowest value was 0.3336. Here, the

relatively high standard deviation value as well as

the maximum and minimum values indicates the

increasing need for the human element, especially

since the maximum value.

In Table 5, the autocorrelation problem appears

in the model if adjacent views are interconnected,

which will affect the validity of the model, and

therefore the effect of independent variables on the

dependent variable will be greatly increased due to

that correlation. To verify this, the Durbin-Watson

(D-W) test was used. The value (DW) is calculated

according to a complex relationship, and it is

obtained through statistical programs. After

calculating the value (DW), it is compared with the

two tabulated values (DL), which represents the

minimum lack of autocorrelation, and (DV), which

represents the maximum lack of autocorrelation,

depending on the number of observations and the

number of independent variables in the model for

each level of significance, and one of the two

hypotheses is accepted or rejected based on some

mathematical rules. The value of the median (DW)

is two, and when there is no autocorrelation, the

correlation coefficient is equal to zero. In addition,

the nihilistic hypothesis (H0) is accepted or rejected

based on some statistical comparisons.

Table 5. Autocorrelation Test

Hypotheses

Calculated

D-W value

DV.

Result

1-1

1.92

0.61

1.4

There is no

autocorrelation

problem

H01-2

1.666

0.61

1.4

There is no

autocorrelation

problem

H01-3

2.736

0.61

1.4

There is no

autocorrelation

problem

H01-4

2.893

0.61

1.4

There is no

autocorrelation

problem

The Durbin-Watson null hypothesis and the alternative

hypothesis will be tested: H0: The model has no

autocorrelation problem. Ha: Autocorrelation exists in the

model.

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Mohammad Mahmoud Humeedat,

Khawla Kassed Abdo,

Hanan Ahmad Qudah, Emilio Martín

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Volume 20, 2023

We observe that the D-W values of the variables in

all of the hypotheses are greater than dV, indicating

that the data are free of autocorrelation and that

there is no correlation between the random error

limits in the regression model. We also note that the

value of (DW) does not fall within the range, since

(dL<dV), and therefore the model does not suffer

from the autocorrelation problem.

The strength of the general linear model

depends mainly on the hypothesis of the

independence of each of the independent variables.

If this condition is not met, the model suffers from

the problem of linear interference

(multicollinearity), then the general linear model is

then not suitable for the application, and it cannot be

considered good for the process of estimating

parameters. To achieve this, the Collinearity

Diagnostics scale is used by calculating the

coefficient of variation Inflation Factor (VIF) for

each of the independent variables. This test is a

measure of the effect of correlation between

independent variables. According to [2] showed that

obtaining a value (VIF) higher than (10) indicates

the existence of a problem of linear multiplicity of

the independent variable in question. It is shown in

the following Table 6, 7, 8, 9.

Table 6. Multiple linear correlations testing of

financial perspective variables

Variable

Variance Inflation

Factor (VIF)

Earnings Per Share

2.733

Return on Assets

1.232

Return on Equity

3.9

From Table 6. It is noted that the value (VIF) of the

independent variables of the financial dimension is below

(10), this indicates the absence of the problem of multiple

linear correlations between the variables.

Table 7. Multiple linear correlations testing of

customer perspective variables

Variable

Variance Inflation

Factor (VIF)

Sales growth

3.373

Market share

3.345

From Table 7. It is noted that the value (VIF) of the

independent variables of the financial dimension is below

(10), this indicates the absence of the problem of multiple

linear correlations between the variables

Table 8. Multiple linear correlations testing of

internal-business-process perspective variables

Variable

Variance Inflation Factor

(VIF)

Research and Development

Costs to Sales

3.111

Asset turnover rate

2.033

From Table 8. It is noted that the value (VIF) of the

independent variables of the financial dimension is below

(10), this indicates the absence of the problem of multiple

linear correlations between the variables

Table 9. Multiple linear correlations testing of

variables related to learning and growth perspectives

Variable

Variance Inflation Factor

(VIF)

The ratio of training

expenses to sales

2.053

The ratio of salaries to

expenses

4.053

From Table 9. It is noted that the value (VIF) of the

independent variables of the financial dimension is below

(10), this indicates the absence of the problem of multiple

linear correlations between the variables

The study hypotheses were subjected to multiple

linear regression analysis using EVIEWS software,

and the results were as follows: Main hypothesis

H0: There is no statistically significant effect at the

significance level (α ≤ 0.05) of applying the

balanced scorecard on intellectual capital. The sub-

hypotheses of this hypothesis were subjected to

multiple regression analysis, and the results were as

follows:

First sub-hypothesis H01: There is no

statistically significant effect at the significance

level (α≤0.05) of applying the financial perspective

of the balanced scorecard on intellectual capital.

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Laith Akram Al-Qudah,

Mohammad Mahmoud Humeedat,

Khawla Kassed Abdo,

Hanan Ahmad Qudah, Emilio Martín

E-ISSN: 2224-2899

896

Volume 20, 2023

Table 10. Results of testing the impact of the application of the financial perspective on intellectual capital

The

dependent

variable

Adjusted

Sig F*

Regression coefficient

Standard

error

Sig t*

Intellectual

capital

0.816

0.735

22.302

0.043

Return

on assets

-2261

397.47

5.688

0.03

Return

Equity

137.26

38.233

3.59

0.07

Constant

slope

13.515

7.613

1.775

0.218

* The effect is statistically significant at the level of (α ≤ 0.05)

The results of Table 10, indicate that the effect

of the independent variables of the financial

perspective on the dependent variable (intellectual

capital) is statistically significant, where the

calculated value of F was (22.302), at a significant

level (Sig F = 0.043), which is less than 0.05, and

the value of the determination coefficient was (R2 =

0.816), which indicates that (81.6%) of the variation

in (intellectual capital) is due to the other variables

being constant. The high value of the determination

coefficient is due to the small sample size of the

study and the period of the study, which require

high values of the determination coefficient to reach

the significance of the effect. As for the regression

coefficient β=-2260.71, it indicates the effect of the

return on intellectual capital assets, which is a

significant effect, where the value of t was (-5.688)

and at an indicative level (Sig = 0.030), and the

value of the regression coefficient at the return on

revenue β=137.259 ( it indicates the effect of that

variable, which is not significant, where the value of

t was (3.590) and at an indicative level (Sig =

0.070), we, therefore, reject the first sub-hypothesis

and accept the alternative, which states that: There

is a statistically significant effect at the level of

(α≤0.05) for the application of the financial

perspective of the balanced scorecard on intellectual

capital. Depending on Table 10, the relationship

between the model variables can be written as

follows:

IC1 = 13.515 – 2260.710 (ROA) + 137.259 (ROE)

Our first result suggests there is no statistically

significant effect at the significance level (α≤0.05)

of applying the financial perspective of the balanced

scorecard on intellectual capital. Furthermore, the

financial perspective of the balanced scorecard can

be used to assess the value of intellectual capital.

One approach is to track the financial returns

generated by the organization's intellectual capital,

such as the revenue or profits generated by new

products or services that are developed using

intellectual capital. Another approach is to measure

the value of intellectual capital through intangible

asset valuation methods, such as the market or

income approach, which can be used to estimate the

present value of future cash flows generated by

intellectual capital.

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Mohammad Mahmoud Humeedat,

Khawla Kassed Abdo,

Hanan Ahmad Qudah, Emilio Martín

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Table 11. Results of testing the impact of the application of the customer perspective on intellectual capital

The

dependent

variable

Adjusted

Sig

Regression coefficient

Standard

error

Sig

Intellectual

capital

0.892

0.886

291.04

0.003

The ratio

marketing

expenses

to sales

3471.5

148.83

23.326

0.002

Sales

growth

-6.516

1.297

-5.026

0.037

Constant

slope

-38.31

3.366

-11.38

0.008

* The effect is statistically significant at the level of (α ≤ 0.05)

This discovery is in line with the findings of [1],

[10], [35]. This discovery, however, contradicts the

conclusions of [4], [6], [11].Second sub-hypothesis

H02: There is no statistically significant effect at the

level (α≤0.05) of applying the customer perspective

of the balanced scorecard on intellectual capital.

The results of Table 11, indicate that the effect

of the independent variables of the customer

perspective on the dependent variable (intellectual

capital efficiency) is statistically significant, where

the calculated value of F was (291.036), at the

significance level (Sig F = 0.003), which is less than

0.05, and the value of the determination coefficient

was (R2 = 0.892), which indicates that 89.2% of the

variation in (intellectual capital efficiency) can be

explained all other variables are constant. The high

value of the determination coefficient is due to the

small sample size of the study and the period of the

study, which require high values of the

determination coefficient to reach the significance

of the effect. As for the regression coefficient

β=3471.542, it indicates the effect of the ratio of

marketing expenses to revenues on intellectual

capital, which is a significant effect, where the value

of t was (23.326) and at an indicative level (Sig =

0.002), and the value of the regression coefficient

(β=-6.516) when revenue grew, it indicates the

effect of that variable, which is a significant effect,

where the value of t was (-5.026) and at the level

denotation (Sig = 0.037), and therefore we reject the

second sub-hypothesis and accept the alternative,

which states that: "There is a statistically significant

effect at the level of (α≤0.05) for the application of

the customer perspective of the balanced scorecard

on intellectual capital." Depending on Table 11, the

relationship between the model variables can be

written as follows:

IC2 = -38.314 +3471.542 (ME-REV) -6.516 (SG)

Our second result suggests there is no

statistically significant effect at the significance

level (α≤0.05) of applying the customer perspective

of the balanced scorecard on intellectual capital.

Furthermore, the customer perspective of the

balanced scorecard can be used to assess the value

of intellectual capital. One approach is to track the

customer-related metrics that are influenced by

intellectual capital, such as customer satisfaction or

customer retention. Another approach is to conduct

customer surveys or focus groups to gather feedback

on the organization's products and services and how

they are meeting the needs and expectations of

customers. It can help the organization identify

areas where intellectual capital can be used to

improve the customer experience and drive

customer satisfaction. This discovery is in line with

the findings of [9], [28]. This discovery, however,

contradicts the conclusions of [27], [33].

Third sub-hypothesis H03: There is no

statistically significant effect at the level (α≤0.05) of

applying the internal-business-process perspective

of the balanced scorecard on intellectual capital.

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Hanan Ahmad Qudah, Emilio Martín

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Table 12. Results of testing the impact of the application of the internal-business-process perspective on

intellectual capital

The

dependent

variable

Adjusted

Sig

Regression coefficient

Standard

error

Sig

Intellectual

capital

0.872

0.845

69.636

0.014

Research

and

development

costs to sales

10957

2526.3

4.337

0.049

Asset

turnover rate

-2792

278.62

10.02

0.01

Constant

slope

130.52

19.034

6.857

0.021

* The effect is statistically significant at the level of (α ≤ 0.05)

The results of Table 12 indicate that the effect of the

independent variables of the internal-business-

process perspective on the dependent variable

(efficiency of intellectual capital) is statistically

significant, where the calculated value of F was

(69.636) and at the level of significance (Sig F =

0.014), which is less than 0.05, and the value of the

determination coefficient was (R2 = 0.872), it

indicates that (87.2%) of the variation in (efficiency

of intellectual capital) is due to all other variables

being constant. The high value of the determination

coefficient is due to the small sample size of the

study and the period of the study, which require

high values of the determination coefficient to reach

the significance of the effect. As for the regression

coefficient β=10956.8, it indicates the impact of the

ratio of R & D expenses to revenues on intellectual

capital, which is a significant effect, where the value

of t was (2526.299) and at a significant level (Sig =

0.049), and the value of the regression coefficient at

the asset turnover β=-2792.02 (it indicates the

impact of that variable, which is a significant effect,

where the value of t -10.021) at the level of

significance (sig = 0.010), and therefore we reject

the third sub-hypothesis and accept the alternative,

which states that: "There is a statistically significant

effect at the level of (α≤0.05) for the application of

the internal-business-process perspective of the

balanced scorecard on intellectual capital.

Depending on Table 12, the relationship between

the model variables can be written as follows:

IC3 = 130.517 +10956.8 (R&D-REV) -2792.02

(ATO)

Our third result suggests there is no statistically

significant effect at the significance level (α≤0.05)

of applying the internal-business-process

perspective of the balanced scorecard on intellectual

capital. Furthermore, the internal-business-process

perspective of the balanced scorecard can be used to

assess the value of intellectual capital. One approach

is to track the internal-business-process metrics that

are influenced by intellectual capital, such as

process efficiency or process effectiveness. Another

approach is to conduct process audits or process

improvement initiatives to identify areas where

intellectual capital can be used to optimize the

organization's core business processes. This can

help the organization identify areas where

intellectual capital can be used to improve process

efficiency, effectiveness, and innovation. This

discovery is in line with the findings of [9], [35].

This discovery, however, contradicts the

conclusions of [11], [27].

Fourth sub-hypothesis H04: There is no statistically

significant effect at the level (α≤0.05) of applying

the learning and growth perspective of the balanced

scorecard on intellectual capital.

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Table 13. Results of testing the impact of applying the learning and growth perspective on intellectual capital

The

dependent

variable

Adjusted

Sig

Regression coefficient

Standard

error

Sig t*

intellectual

capital

0.745

0.604

11.395

0.081

Training

expenses

for sales

-4102

25022

0.164

0.885

Salaries to

expenses

-245.2

53.176

4.612

0.044

Constant

slope

91.136

21.235

4.292

0.05

* The effect is statistically significant at the level of (α ≤ 0.05)

The results of Table 13 indicate that the effect of the

independent variables of the learning and growth

perspective on the dependent variable (intellectual

capital efficiency) is statistically non-significant,

where the calculated value of F was (11.395) and at

the level of significance (Sig F = 0.081), which is

greater than 0.05, and the value of the determination

coefficient was (R2 = 0.745), which indicates that

(74.5%) of the variation in (intellectual capital

efficiency) can be explained by the variation in the

model variables, with all other variables remaining

constant. As for the regression coefficient β=-

4101.698, it indicates the effect of the ratio of

training expenses to revenues on intellectual capital,

which is an insignificant effect, where the value of t

was (-0.164) and at the level of significance (Sig =

0.885), and the value of the regression coefficient at

salaries to expenses β=-245.233 (it indicates the

effect of that variable, which is a significant effect,

where the value of t -4.612 at the level of

significance (sig = 0.044), and therefore we accept

the fourth sub-hypothesis, which states that: "There

is no statistically significant effect at the level of

(α≤0.05) of applying the learning and growth

perspective of the balanced scorecard on intellectual

capital. Depending on Table 13, it is not possible to

write down the relationship between the model

variables.

IC4 = 91.136 +-4102 (TREXP) -245.2 (SAEXP)

Our fourth result suggests there is no statistically

significant effect at the significance level (α≤0.05)

of applying the internal-business-process

perspective of the balanced scorecard on intellectual

capital. Furthermore, the learning and growth

perspective of the balanced scorecard can be used to

assess the value of intellectual capital. One approach

is to track the learning and growth metrics that are

influenced by intellectual capital, such as employee

satisfaction or employee retention. Another

approach is to invest in employee training and

development programs that help to build and

enhance the organization's intellectual capital. This

can help the organization to develop the knowledge,

skills, and expertise that are needed to support

future growth and success. This discovery is in line

with the findings of, [6], [7]. This discovery,

however, contradicts the conclusions of, [9], [40].

5 Conclusion

In general, every measuring model or instrument

must be used appropriately and in a balanced

manner. While the Balanced Scorecard scale may be

a valuable tool for estimating intellectual capital, it

is critical to examine the company's particular

context and needs and to apply the model in a

manner that is compatible with the company's

strategic goals and objectives. Excessive use of the

model, or dependence on it at the expense of other

relevant criteria, might result in skewed or

erroneous forecasts of intellectual capital. It is also

critical to assess any potential biases or limits of the

measuring model and take action to reduce their

influence on the results.

This study's findings show that when utilizing the

balanced scorecard scale to create a four-track

measuring model to forecast intellectual capital,

practically all factors are positive. The research

recommends that you determine the essential

elements of intellectual capital that are most

relevant to your sector and market conditions.

Considerations may include the company's

competitive edge, strategic goals, and the specific

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Hanan Ahmad Qudah, Emilio Martín

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qualities of its business model. Create a collection

of measurements or indicators that may be used to

assess the success of each dimension of intellectual

capital in the future. These indicators should be

relevant and actionable, and they should be

connected with the company's strategic goals. Using

suiTable statistical and analytical approaches collect

and evaluate data on the relevant parameters. This

may entail collecting information from a range of

sources, like financial documents, consumer

surveys, staff performance reviews, and other

relevant data sources. Furthermore, utilize the

analysis findings to spot patterns and trends in the

data and make educated judgments on the

company's intellectual capital. This may entail

comparing the company's performance to industry

benchmarks or similar firms' performance.

Subsequently, the subjectivity of the criteria

employed to assess intellectual capital may restrict

the credibility of the Balanced Scorecard scale.

Individuals may interpret the metrics differently,

resulting in conflicting findings. Subsequently, the

Balanced Scorecard scale's validity may be

constrained by the fact that it is supposed to assess

both financial and non-financial performance. While

these indicators may be significant to intellectual

capital, they may not fully capture all of its

characteristics. Subsequently, data on the

performance of industrial joint stock businesses

listed on the Amman Stock Exchange may be

restricted, and the analysis's accuracy and

thoroughness may suffer as a result.

Determine the primary intellectual capital drivers

for these firms. These may include elements such as

the company's human capital's quality and diversity,

the depth of its customer ties, the efficacy of its

internal procedures, and the organization's

innovation and learning. Create a set of metrics to

track each of these factors as well. These indicators

should be measurable and related to the vision and

strategy of the firm. Use the insights gathered from

your investigation to make recommendations for

increasing these firms' intellectual capital. This

might include adjusting the company's human

capital management procedures, improving

customer connections, or focusing more on

innovation and learning.

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DOI: 10.37394/23207.2023.20.82

Laith Akram Al-Qudah,

Mohammad Mahmoud Humeedat,

Khawla Kassed Abdo,

Hanan Ahmad Qudah, Emilio Martín

E-ISSN: 2224-2899

901

Volume 20, 2023

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DOI: 10.37394/23207.2023.20.82

Laith Akram Al-Qudah,

Mohammad Mahmoud Humeedat,

Khawla Kassed Abdo,

Hanan Ahmad Qudah, Emilio Martín

E-ISSN: 2224-2899

902

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DOI: 10.37394/23207.2023.20.82

Laith Akram Al-Qudah,

Mohammad Mahmoud Humeedat,

Khawla Kassed Abdo,

Hanan Ahmad Qudah, Emilio Martín

E-ISSN: 2224-2899

903

Volume 20, 2023

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Contribution of Individual Authors to the

Creation of a Scientific Article (Ghostwriting

Policy)

-Laith Akram Al-Qadah, Hanan Ahmad Qudah and

Emilio Martin Conducted the Simulation, Original

Writing and Optimization.

-Mohammad Mahmoud Humeedat and Khawla

Kissed Abdo Has Implemented Statistical Analysis.

-Hanan Ahmad Qudah has Organized and Executed

the Experiments of Section 4.

-Laith Akram Al-Qadah was Responsible for the

Conclusion.

Sources of Funding for Research Presented in a

Scientific Article or Scientific Article Itself

No funding was received for conducting this study.

Conflict of Interest

The authors have no conflict of interest to declare.

Creative Commons Attribution License 4.0

(Attribution 4.0 International, CC BY 4.0)

This article is published under the terms of the

Creative Commons Attribution License 4.0

https://creativecommons.org/licenses/by/4.0/deed.en

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DOI: 10.37394/23207.2023.20.82

Laith Akram Al-Qudah,

Mohammad Mahmoud Humeedat,

Khawla Kassed Abdo,

Hanan Ahmad Qudah, Emilio Martín

E-ISSN: 2224-2899

904

Volume 20, 2023