Investigating the Effect of Digital Transformation on the Energy Sector:
Saudi Arabia’s Case
YOUSIF OSMAN*, ISAM ELLAYTHY, YAHYA DAGHRI
Economics Department, School of Business,
King Faisal University,
KINGDOM OF SAUDI ARABIA
*Corresponding Author
Abstract: - In many countries of the world the demand for energy and reliability of renewable energy has risen
remarkably with the digital transformation of modern times. For instance, digital technologies such as the
Internet of Things, adoption index, and big data analytics are applied to increase efficiency and productivity in
the energy sector, along with identifying important interfaces and infrastructure necessary for the efficient and
smart functioning of operators and operations, as well as curating an increase in the reliability of tasks and
operations while optimizing costs. The purpose of this study is to explore how the integration of digital
technologies in the energy sector in Saudi Arabia has led to increased productivity and sustainability on a larger
scale. The study focuses on reducing operational costs and improving asset management through digital
solutions for monitoring and maintenance. The study covers the period from 2015 to 2022. The findings of the
study show that the digital adoption index positively affects GDP growth in the short term with error correction
term, but not in the long term without error correction term. Furthermore, the findings indicate that there is no
statistically significant relationship between independent variables and GDP growth according to estimates of
the long-term results. The null hypothesis is accepted indicating no cointegration between independent
variables and GDP growth based on F-statistics being less than I (0) at a 1% significance level. Finally, the trail
to explore the nexus of digital technologies and the energy sector in Saudi Arabia is, so far, a new attempt in
this area. This is an indication of the originality of this research paper.
Key-Words: - Digital Transformation; Energy Sector; ARDL model; Saudi Arabia; adoption index; GDP
growth; operational costs.
Received: June 16, 2023. Revised: October 15, 2023. Accepted: November 19, 2023. Available online: December 15, 2023.
1 Introduction
The integration of digital aspects in the energy
sector has improved productivity and sustainability
on a larger scale. The demand for energy and the
reliability of renewable energy has increased with
the digital transformation of modern times. Hence,
the output and the financial services of the energy
sector are elaborating with the hands of
digitalization. Therefore, the present study will be
based on the economics of digital transformation in
the energy sector in the Kingdom of Saudi Arabia
over the period from 2015 to 2022.
Future growth of the energy sector should be
primarily driven by digital technology, platform
solutions, efficiency, and safety. The electric power
business can raise its production capacity, develop
new uses for energy resources, boost its efficiency,
and improve logistics thanks to emerging digital
technology.
Digital transformation in the energy sector in
the Kingdom of Saudi Arabia is a key priority for
the government. The Kingdom has been investing
heavily in digital technologies to drive innovation
and efficiency across the energy sector. The
government has launched several initiatives to
promote digital transformation, such as the
National Transformation Program (NTP), which
aims to modernize and digitize the public sector.
Additionally, Saudi Aramco has launched its digital
transformation program, which focuses on
improving operational efficiency and customer
experience. The government is also investing in
smart grid technology, which will enable better
monitoring and control of energy consumption.
This will help reduce energy waste and improve
efficiency. Furthermore, the government is
investing in renewable energy sources such as solar
and wind power, which will help reduce reliance on
fossil fuels.
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In addition to these initiatives, the government
is also encouraging private companies to invest in
digital technologies for the energy sector. Private
companies are encouraged to develop innovative
solutions that can improve efficiency and reduce
costs for consumers. For example, companies are
developing software solutions that can monitor
energy usage and provide customers with real-time
data about their consumption patterns. This data
can then be used to optimize energy usage and
reduce costs for consumers.
The primary market participants are modifying
their strategies in response to rising competition,
which is changing methods to energy market
regulation and the composition of utilized energy
resources. The ongoing procedures are crucial for
the Kingdom of Saudi Arabia because their export
revenue is dependent on them. The global energy
market will need to find new equilibrium points
considering the continuing changes, and the
Kingdom of Saudi Arabia will need to properly
prioritize its future development. The investment of
businesses, as well as the recruitment of foreign
investment for the realization of projects connected
to the introduction of digital technologies, is a
crucial factor in the development of the energy
sector in the Kingdom of Saudi Arabia.
In the same context, digital transformation in
the energy sector can help increase efficiency by
streamlining processes and reducing manual labor.
This can lead to cost savings and improved
customer service. Also, digital transformation can
help improve security by providing better data
protection and monitoring of energy systems. This
can help reduce the risk of cyber-attacks and other
security threats. Digital transformation can also
help increase reliability by providing real-time
monitoring of energy systems, which can help
reduce downtime and improve customer
satisfaction.
Digital transformation in the energy sector can
also help improve sustainability by reducing
emissions and increasing renewable energy
sources. This can help reduce the environmental
impact of energy production and consumption.
The next sections of this research are organized
as follows. Section 2 begins with a description of
the research problem including the research
objective and research hypothesis. A review of
previous studies and literature on the research topic
is presented in section 3. Section 4 focuses on
sketching the data and study model (ARDL) along
with its econometric analytical techniques to test
the research hypothesis. Section 5 provides details
of the research results regarding the impact of
digital transformers on the economic growth in the
energy sector in the Kingdom of Saudi Arabia in
the short-run and long-run as well. Finally, section
6 is devoted to the discussion and conclusion of the
research.
2 Statement of the Problem
The digital transformation process in the Kingdom
of Saudi Arabia faces a set of problems, and we
will mention the most important to bridge this gap,
our study will address the following aspects
question:
a. What are the necessary interfaces for
enabling the smart functioning of the
operators?
b. How does digital transformation improve
economic development in the Kingdom of
Saudi Arabia?
2.1 Research Aim and Objective
The study aims to:
stating the most important and necessary
interfaces and infrastructure for enabling the
efficient and smart functioning of the
operators and operations.
curating an increase in the reliability of tasks
and operations conducted in the energy sector
and optimization of cost.
digital transformation has the objective of
transitioning toward a renewable and low-
carbon economy.
2.2 Research Hypotheses
This research addresses two hypotheses:
Hypothesis 1 (H1):
There is no relationship between economic growth
and digital transformers in the energy industry.
Hypothesis 2 (H2):
There is no significant impact of the digital
transformers on the economic growth in the energy
industry.
3 Literature Review
3.1 The Association between Digitalization,
Economic Growth and Energy Sector
Many people all over the world are lacking
electricity at present times. Therefore, the
digitalization of electricity will reduce the cost of
electricity on a larger scale, [1]. On the other hand,
it has been noted that the revenue and sales of the
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electric sector have been enhanced while using
digitalization. In the Kingdom of Saudi Arabia, the
scope of the digital economy has been improved
while using AI, big data, IoT, robotics, 5G, and
machine learning across both private and public
sectors of electricity, [2]. It has been noted that the
business operations of the energy sector become
easier while making digital transformation
effective. The chances of mistakes are reduced at
the same time with the incorporation of digital
technology.
The initiatives of the $1.2 billion have been
used by the youngsters of Saudi Arabia while
incorporating the digital aspects in the energy
sector, [3]. At the same time, it has been observed
that the manpower of the business sector reduces
while undertaking the aspects of digitalization in
the energy sector. The systems with digital
acquisition make more business operations at a
time which is fruitful for the respective business
growth of the economic sector, [4]. In this way,
digital technology enhances the productivity of the
energy sector which has an effective role in
economic growth. On the contrary, the labor cost of
the energy sector decreases with the help of digital
transformation.
Digitalization has been suggested to be a key
factor in economic progress in developing nations,
increasing capital and worker productivity,
lowering transaction costs, and allowing access to
international markets, [5]. Access to goods and
services at cheaper prices is made easier by new
technology. It's possible for developing economies
to advance faster than industrialized ones. Another
example of how the lack of banks in rural regions
has made it easier for most impoverished people to
receive financial services is mobile banking.
Kenya is an example of a country in Sub-
Saharan Africa where mobile banking has made it
possible for individuals to transition out of
agriculture and into non-farm companies, which
may eventually result in higher per capita
consumption levels and reduced poverty. In
developed countries, digital advancements have
higher prospects thanks to network effects in
nations with larger networks. Network effects may
therefore provide industrialised nations more
benefits.
According to, [6], digitalization boosts
economic growth through (a) meeting consumer
demand for digital goods including computers,
communications equipment, and software, and (b)
boosting productivity and investments in ICT-using
industries. The association between digitalization
and economic growth as a comparative analysis of
Sub-Saharan Africa and OECD economies is
discussed by, [7]. The authors compare Sub-
Saharan Africa’s (SSA) economic growth to that of
OECD nations to determine the impact of
digitalization on SSA’s economic growth. When
measuring the consequences of digitalization, it is
important to compare the most and least developed
nations to determine whether these effects are
dependent on the degree of development of the
respective nations.
The adoption of new technologies is thought to
have had a significant impact on economic activity
in Sub-Saharan Africa. These technologies
included e-commerce participation by small and
medium-sized enterprises (SMEs), accommodation
of the poor majority who were initially financially
excluded from mobile banking, and communication
accessibility which was hampered by poor
infrastructure.
On the other hand, the least developed nations
in SSA have been experiencing early
deindustrialization because of the consequences of
digitalization. In this work, we utilize generalized
linear methods of moments (GMM) estimators
using a panel dataset including 41 SSA and 33
OECD nations over 11 years, from 2006 to 2016.
The findings demonstrate that digitization
contributes favorably to economic growth in both
sets of nations. Comparing SSA to OECD nations,
the impact of broadband internet is negligible,
however, the impact of mobile telecommunications
is greater in SSA.
According to the authors, these findings are
especially intriguing because less developed
nations have greater chances due to less
technological stagnation. Regarding the policy
ramifications, this study suggests that SSA
governments increase their investments in ICT and
other infrastructures to take advantage of
digitalization and achieve meaningful economic
growth.
3.2 Enhancement of the Efficiency of the
Work Operations
The efficiency of the respective work operations is
enhanced with the hands of digital technology, [3].
Hence, it has been noted that the energy sectors of
the globalized world are using the theory of digital
transformation to obtain better results in their
business operations daily. The four main aspects of
digital transformation are included with process
transformation, domain transformation, the
transformation of business model, and cultural
transformation. The process transformation
improves the quality of the entire business
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operations. The study evaluates the literature that
has already been written about the use of IoT in
energy systems in general and smart grids.
Additionally, we go through IoT-enabling
technologies including cloud computing and
various platforms for data processing. They also
discuss privacy and security concerns that arise
when using IoT in the energy sector, as well as
some potential solutions, such as blockchain
technology. The study offers an overview of the
role of IoT in energy system optimization for
energy policymakers, energy economists, and
managers.
Besides this, the cultural transformation will
evoke the maintenance of cultural diversity on a
broader aspect. Therefore, the transformation of the
domain increases the demographic boundaries of
the respective business operations. It can be stated
that the application of digital technology in the
electronics sector will create 3.45 million new jobs
between the years 2016 and 2025, [8]. Therefore, a
10.7% growth in the job will be noticed in the
electricity industry shortly.
The application of renewable energy will be
increased with the digital transformation of the
present times. In this way, the energy sector will
effectively create 1.07 million new jobs shortly.
The management of asset performance and
automation will be noticed extensively on a larger
scale with the incorporation of efficient digital
technologies, [4]. Hence, new employees will be
added who have many talents, proper training, and
skills in digitized technology. The process of
globalization of the energy sector will be noted thus
while making business operations with digitized
technology.
3.3 Improvement of the Customer Service
It has been observed that the use of digital
technology has enhanced the aspects of customer
services on a larger scale. As the demands and
expectations of modern customers are changing day
by day, the employees of the energy sector are trying
to make business operations accordingly, [9].
Besides this, the tendency to use online marketing
has been evoked within the customers. They are
spending more time on online platforms. Therefore,
attractive advertisements and easy and frequent
feedback from the managing professionals can be
noticed while making business operations online.
However, satisfied customers provide positive
feedback on the online platforms of the energy
sector. The number of likes, shares, and comments
will be increased on a larger scale with the hands of
happy and content customers of the respective
energy sector, [10]. Both the business growth and
profitability of the business organization will be
noticed.
In this way, an integrated service of the
customers will be noticed with the incorporation of
digitally enabled services and products. The
generation of energy and its management becomes
easier when using digital technology in the energy
sector of the modern world. Moreover,
empowerment will be built among the customers
for managing their electric consumption on a
broader aspect, [2]. The prevention of unwanted
incidents is noticed with the hands of digitalized
technologies in the energy sectors on a larger
scope. Therefore, the study notes effective supply
chain management with the hands of digitalization
in the energy sectors on a global aspect. It has been
noted that customers obtain electric services and
products without any barriers or problems in the
contemporary situation. As digital technology
changes the habits of the customers, they get the
energy services exactly at the time of their urgency,
[10].
On the other hand, the integration of energy
and the management of energy services are noticed
exaggeratedly on a larger container with the
application of digital technology.
3.4 The Difficulties of using Digital
Technologies in the Energy Sector
It has been already discussed that the energy sector
is adopting digital technology to increase efficiency
and production, but some difficulties are there in
the pathway of implementation, [8]. Digitalization
implies the inclusion of the latest technologies as
per the requirements of the energy producers.
Technological innovation intensifies the threat of
data stealing and hacking. The energy sectors
around the world will implement tech-based supply
chains, but the digital supply chain is vulnerable to
hacking. On the other hand, the digital economy
depends upon tech-savvy people. The authors
discussed that the hazards and potential of these
technologies for environmental sustainability, as
well as political awareness of these risks and
opportunities, become increasingly essential with
the growing use of information and communication
technologies (ICTs) in industrial production.
In this study, we examined the digital and
industrial policies of three East Asian and Pacific
nations—China, Thailand, and the Philippines—as
well as four Sub-Saharan African nations—South
Africa, Rwanda, Kenya, and Nigeria—about their
expectations regarding the effects of ICTs on
industry for environmental sustainability. We
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expanded upon already-existing frameworks for the
evaluation of ICTs that make a distinction between
direct environmental effects that happen throughout
the ICT's lifecycle and indirect environmental
effects that come about because of the use of ICTs
in a variety of production processes and economic
activities. We investigated and analyzed policy
expectations for both the direct and indirect
environmental implications of ICTs in the industry
using qualitative content analysis.
Also, [8], showed in their study that policies
focus more on the good indirect effects of ICT use,
such as improved energy efficiency and resource
management than on the negative direct effects of
ICTs, such as ICT power consumption.
Furthermore, expectations varied among nations,
and there was no overarching theme that appeared
in all regulations. We propose that policymakers
should include a deeper systemic knowledge of
interconnected direct and indirect consequences
and seek targeted initiatives to use ICTs as
instruments for environmentally friendly sectors,
going beyond awareness of specific potential.
Although businesses should employ digital
technologies more often to boost or preserve their
competitive edge, they may run across obstacles
and problems that prevent them from fully
embracing digitalization. As a result, required
measures must be taken to address any potential
issues, [11]. For instance, despite the oil and gas
industry's enthusiasm for adopting BDA, the most
difficult difficulties facing the sector are a lack of
business support and understanding about big data,
the complexity of its applications, and the quality
of the data, [12].
Workforce scarcity can create another
challenge for the energy sector. A high percentage
of labor turnover can impact the growth of the
energy sector on a large scale. Carbon footprint
reduction is the central concern of the energy sector
in the modern period. In this situation, the
traditional infrastructure should be replaced by
tech-based services. The replacement cost seems to
be a burden for organizations. The convenient
equipment will be thrown away, which will also
cause environmental degradation, [10]. While
investigating the digital economy, it is understood
that recycling is mandatory to minimize energy
consumption. The depletion of natural resources
and accumulation of waste are threatening
sustainability.
4 Research Methodology
The process used in the research methodology is
depicted in Figure 1:
Fig. 1: Steps of Applied Study
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4.1 Data
The sample period covers the years from 2015 to
2022. All data used for the variables of this study
are secondary data sourced from the World Bank's
“World Development Indicators (WDI) database”,
the Kingdom of Saudi Arabia's statistical annual
yearbook reports various years.
Table 1. Variables’ definition and description
Variable
Notation
Description and source
Unit
The economic growth
: represents the economic growth and
can be calculated by the formula
When GDP, is GDP per capita in
the Kingdom of Saudi Arabia,
The human capital
The human capital factor will be calculated
by dividing the number of people of working
age by the total population of the Kingdom
of Saudi Arabia
Percentage of
the population
The total invested capital
Stands for the investment capital factor
which is determined by dividing the total
invested capital by the gross domestic
product in the Kingdom of Saudi Arabia.
% of GDP
The digital adoption index
Stands for the digital adoption index of the
Kingdom of Saudi Arabia
The Government expenditure per GDP
Stands for the Government expenditure per
GDP of the Kingdom of Saudi Arabia.
% of GDP
Trade ratio per GDP
Import-export ratio per GDP of the Kingdom
of Saudi Arabia.
% of GDP
Source: World Bank
Regarding the stands for the digital
adoption index of the Kingdom of Saudi Arabia,
reports for digitalization various years, [13]. Table
1 presents the description of the variables, the study
data is expanded to monthly data using methods of,
[14], [15].
4.2 The Econometric Model
A simple regression model will then be applied for
analysis of the linear relationship between digital
transformation and economic growth as the
fundamental econometric model. This will help to
generate the best-fit line between the two available
data sets and then evaluate these datasets to analyze
how far each of the different data points is
distributed from one another. Eviews (V.12)
software packages will be used for further
statistical analysis. Assess the risk that correlations
can develop accidentally, these software tools can
also quickly evaluate for statistical significance. A
variety of techniques to gauge the reliability of the
predictions made will also be done, including R-
squared, t-tests, p-values, and null hypothesis
testing.
The impact of digital transformation on
economic growth can be assessed by using a model
adapted from the Cobb-Douglas function. The
Cobb-Douglas function is as follows:
When , is the economic growth in the Kingdom
of Saudi Arabia. The number of persons who are
working age divided by the overall population will
be used to compute the human capital factor ().
Additionally, the investment capital component
() is calculated by dividing the entire amount of
invested capital by the GDP.
By taking the Cobb-Douglas function's natural
logarithm, the prior model will be calculated. The
growth model they suggest has the following
structure:
When
: represents the dependent variable which is
calculated by the formula
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when GDP is GDP per capita in the Kingdom of
Saudi Arabia. These elements will not promote
economic growth as they did in the past when
human capital and investment capital were fully
used. There will be a slowing or a decline in
economic growth. Recent empirical work, however,
demonstrates that the digital transition is essential
for fostering economic growth. It is concluded that
the growth model, which is represented as follows,
should incorporate the digital transformation
variable () as well as the government
expenditure ( and the trade openness
( ) World Bank database as follows:
so, the variables can be stated as follows:
: represents the economic growth and can be
calculated by the formula
when
GDP is GDP per capita in the Kingdom of Saudi
Arabia, the source of the data is the World Bank
database.
: represents the human capital factor that will be
calculated by dividing the number of people of
working age by the total population in the Kingdom
of Saudi Arabia, the source of the data is the
World Bank database.
: stands for the investment capital factor which is
determined by dividing the total invested capital by
the gross domestic product in the Kingdom of
Saudi Arabia, the source of the data is the World
Bank database.
: stands for the digital adoption index of the
Kingdom of Saudi Arabia, reports for digitalization
various years, [13].
: stands for the Government expenditure per
GDP of the Kingdom of Saudi Arabia, the source
of the data is the World Bank database.
: trade ratio per GDP of the
Kingdom of Saudi Arabia, the statistical World
Bank database.
The Variance Inflation Factor (VIF) test was
carried out before performing the regression
analysis to assess the quality and validity of the
estimated models to make sure that the independent
variables of the study did not suffer from the
problem of multicollinearity as this problem may
result in several unacceptable outcomes such as
inaccurate regression coefficients, failing to reach
statistical significance, changing in the estimated
signs of coefficients, or suboptimal estimates.
Table 2 shows the descriptive statistics for the
dependent and independent variables. The table
presents that the mean of the growth GDP in the
Kingdom of Saudi Arabia over the period from
2015 to 2021 (monthly data) is 0.855248 with a
corresponding standard deviation of 2.868544 and
minimum value of -5.148737 and maximum value
of 5.450610. In the same context, the mean of the
government expenditure over the studying period is
29.56721 with a standard deviation of 3.386348,
and minimum value of 21.28417, and a maximum
value of 36.33841.
Also, the mean of the total invested capital over
the studying period is 28.67747 % with a standard
deviation of 3.214596, and minimum value of
22.75899 %, and a maximum value of 34.94470 %.
Moreover, the mean of the human capital over the
study period is 56.93429 % with a standard
deviation of 2.557821, and minimum value of
53.73279 %, and a maximum value of 61.68677 %.
Regarding the trade ratio, the mean of the variable
over the studying period is 62.20286 % with a
standard deviation of 5.678439, and minimum
value of 50.49131 %, and a maximum value of
73.74334 %. In the same context, the most
important variable in the independent variable is
the digital adoption index has a mean over the
studying period of 80.26145 of the population with
a standard deviation of 56.62212 and a minimum
value of 56.62212 % and a maximum value of
96.04888 %.
The study data can be sketched as follows
(Figure 2):
Table 2. Variables Statistical Descriptive
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variables
GDP_
GROWTH
GET
KT
LT
TRADE
RATIO
DAI
Mean
0.8552
29.567
28.677
56.934
62.202
80.261
Median
1.2069
29.058
28.830
55.941
63.437
89.578
Maximum
5.4506
36.338
34.944
61.686
73.743
96.048
Minimum
-5.1487
21.284
22.758
53.732
50.491
56.622
Std. Dev.
2.8685
3.3863
3.2145
2.5578
5.6784
14.973
Skewness
-0.4301
-0.158
0.1508
0.5393
-0.2573
-0.3815
Kurtosis
2.3722
3.3497
2.4974
1.8724
2.9929
1.3722
Jarque-Bera
3.9691
0.7795
1.2025
8.5223
0.9275
11.311
Probability
0.1374
0.6772
0.5481
0.0141
0.6289
0.0034
Sum
71.840
2483.6
2408.9
4782.4
5225.04
6741.9
Sum Sq. Dev.
682.96
951.79
857.69
543.02
2676.38
18607.9
Observations
96
96
96
96
96
96
-6
-4
-2
0
2
4
6
2015 2016 2017 2018 2019 2020 2021
GDP_GROWTH
20
24
28
32
36
40
2015 2016 2017 2018 2019 2020 2021
GET
20
24
28
32
36
2015 2016 2017 2018 2019 2020 2021
KT
52
54
56
58
60
62
2015 2016 2017 2018 2019 2020 2021
LT
50
55
60
65
70
75
2015 2016 2017 2018 2019 2020 2021
TRADE_RATIO
50
60
70
80
90
100
2015 2016 2017 2018 2019 2020 2021
DAI
Fig. 2: The time series graphs of the study data
4.3 Study Model
This study applied the ARDL model, one of the
most current dynamic models that takes the factor
of time into account, to evaluate the study's
hypotheses using econometrics analytical
technique. to determine the variables' short- and
long-term relationships. As an implicit equation,
the following describes the association between
economic growth and digital transformers, the
Government expenditure per GDP of the Kingdom
of Saudi, in the energy industry as well as the
human capital factor (). Additionally, the
investment capital component ().
=f (() (5)
4.4 Applied Study
The steps that were followed can be illustrated
according to the steps:
4.4.1 Unit-root Test
Table 3 shows the results of unit root tests, We
employ the unit root test of, [16], [17], to assess the
stationarity of time series for the research variables.
Given the results in Table 5, we can use the ARDL
model to determine the short- and long-term
relationships between the study variables because
our variables are mixed between being integrated of
different orders, i.e., order zero, I(0), and order one,
I(1), and there are no variables integrated of order
two, I(2), [18].
6
According to the previous model in equation 6.
It’s expected that all independent variables (digital
transformers, the Government expenditure per GDP
of the Kingdom of Saudi, as well as the human
capital factor (), the investment capital
component () and the trade ratio has positive
associations with dependent variables (GDP
growth). This association can be presented by the
correlation matrix as follows:
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4.4.2 Correlation
A. Correlation Matrix
The linear correlation (multicollinearity) between
the independent variables is one of the most
significant issues that conventional models and
regression analysis face. It has to do with the OLS
method's assumption that the independent variables
in the regression model would not be strongly
correlated with one another, making it difficult to
distinguish between their impacts on the dependent
variable, [19]. There are various signs to look for
this issue, [20]. We may utilize the correlation
matrix between the independent variables and the
variance-inflation factor as one of these indications
(VIF). Severe multicollinearity is indicated by a
VIF of more than 10 and an excess of 0.8 in the
pairwise or zero-order correlation coefficient
between two regressions, [21].
As from Table 4, the Correlation Matrix
indicates that there is no multicollinearity between
independent variables as long as the Pearson
correlation coefficient less is than (0.8). In the
same context, there is a positive correlation
coefficient between economic growth variables in
the Kingdom of Saudi Arabia and the variables
Trade ratio, DAI. On the other hand, there is a
negative correlation coefficient between the
economic growth variable in the Kingdom of
Saudi Arabia and the variables government
expenditure and investment capital factor.
Table 3. Phillips & Perron and Augmented Dickey-Fuller tests for series stationarity
UNIT ROOT TEST TABLE (PP)
variables
GDP
GET
lnKT
lnLT
TRADE
DAI
GROWTH
RATIO
At Level
t-Statistic
-1.5534
-1.1115
-2.5281
5.5842
-0.9749
1.9725
Prob.
0.1125
0.24
0.0119
0.9995
0.2925
0.988
stationarity
No
No
No
No
No
No
At First Difference
t-Statistic
-1.9815
-1.9959
-1.951
-2.017
-2.0433
-1.8096
Prob.
0.0427
0.0446
0.043
0.032
0.02
0.0671
stationarity
Yes
Yes
Yes
Yes
Yes
Yes
UNIT ROOT TEST TABLE (ADF)
At Level
t-Statistic
-2.9123
-0.8655
-1.799
2.0537
-0.8993
1.5597
Prob.
0.0041
0.3381
0.0686
0.9901
0.3237
0.9701
stationarity
Yes
No
No
No
No
No
At First Difference
t-Statistic
-3.5736
-3.727
-3.0569
-1.998
-3.7561
-3.1337
Prob.
0.0005
0.0003
0.0026
0.0407
0.0003
0.0021
stationarity
Yes
Yes
Yes
Yes
Yes
Yes
Notes: (*) Significant at the 10%; (**) Significant at the 5%; (***) Significant at the 1%. and (no) Not Significant
*MacKinnon (1996) one-sided p-values.
Table 4. Correlation Matrix Result pairwise correlations
Probability
GDP
GROWTH
LNKT1
LNLT1
TRADE
RATIO1
DAI1
GET1
GDP_GROWTH1
Correlation
1
LNKT1
Correlation
-0.419910
1
Probability
0.0001
-----
LNLT1
Correlation
0.195708
0.147631
1
Probability
0.0762
0.1829
-----
TRADE_RATIO1
Correlation
0.796142
-0.000679
-0.080445
1
Probability
0.0000
0.9951
0.4697
-----
DAI1
Correlation
0.280050
-0.533336
-0.115267
-0.111008
1
Probability
0.0103
0.0000
0.2994
0.3178
-----
GET1
Correlation
-0.741280
0.314229
-0.187387
-0.698544
0.218410
1
Probability
0.0000
0.0038
0.0898
0.0000
0.0473
-----
After the multicollinearity variables have been
dropped, VIF shows that there is no
multicollinearity between the rest of the
independent variables as long as the variance
coefficient is less than 10 (Table 5).
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B. Variance Inflation Factors (VIF)
Table 5. Variance Inflation Factors (VIF)Test
Results
Variables
VIF
Tolerance
GET1
4.92
0.203
trade ratio1
3.26
0.307
KT1
3.068
0.326
DAI1
2.296
0.436
LT1
1.414
0.707
4.4.3 Optimal Lag Selection
To determine the optimal lag length needed for the
model’s variables present in Table 6, we use
different criteria such as Akaike's Information
Criterion (AIC), Hannan-Quinn Information
Criterion (HIC), and Schwarz Information Criterion
(SIC), [17], as shown in Table 8. Schwarz's
criterion and Akaike's criterion show that the lag
period is the second period.
4.4.4 ARDL Approach
The research utilized the autoregressive distributed
lag (ARDL) approach, a notable method developed
by, [22]. This approach is widely regarded as the
most effective econometric technique when dealing
with variables that are either stationary at I(0) or
integrated of order I(1). Given the specific
objectives of the study, the ARDL model is
considered superior to alternative models in
capturing both the short-term and long-term effects
of digital transformation on the energy sector, we
are using the Bounds Testing Approaches to the
Analysis of Level Relationships and Autoregressive
Distributed-Lag (ARDL) techniques, [22]. One of
the newest dynamic models that considers the
element of time is the ARDL model. We analyze
long-run correlations between variables based on
time series data to pinpoint the short- and long-term
linkages between the variables as well as the rate at
which the system will reach equilibrium. Two
elements make up this model: (1) Autoregressive
(AR) models, which employ the dependent variable
as a lagged independent variable, and (2)
Distributed Lagged (DL) models, which show that
the dependent variable also influences.
Table 6. Optimal Lag Selection Results
Lag
LogL
LR
FPE
AIC
SC
HQ
0
34.97180
NA
1.86e-08
-0.772581
-0.587182
-0.698554
1
1214.395
2138.688
1.07e-21
-31.26387
-29.96608
-30.74568
2
1414.494
330.8304*
1.37e-23*
-35.63985*
-33.22966*
-34.67749*
3
1421.687
10.74075
3.09e-23
-34.87165
-31.34907
-33.46512
4
1430.302
11.48696
7.03e-23
-34.14139
-29.50641
-32.29069
5
1440.869
12.39816
1.62e-22
-33.46316
-27.71579
-31.16830
6
1454.031
13.33738
3.82e-22
-32.85415
-25.99438
-30.11512
7
1470.643
14.17561
9.41e-22
-32.33714
-24.36498
-29.15394
8
1493.013
15.51036
2.43e-21
-31.97369
-22.88913
-28.34633
* Indicates lag order selected by the criterion
LR: sequential modified LR test statistic (each test at 5% level)
FPE: Final prediction error
AIC: Akaike information criterion
SC: Schwarz information criterion
HQ: Hannan-Quinn information criterion
The equation 7 shows the ARDL model for our study,
as follows:
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(7)
where () refers to the first-difference operator; p,
q, r, m, and k indicate lags; (α1 – α5) refers to long-
run parameters; (β1 – β5) refers to short-run
parameters; (α0) refers to the intercept; (µt) refers
to the error term.
The short-run effects are estimated from the
equation 8
β
β
β
β
β
µ
represents the speed of adjustments
towards long-run equilibrium, which means that if
the system is moving out of equilibrium in one
direction, then will pull it back to equilibrium, [17].
“A divergence is shown by a positive coefficient,
and convergence is indicated by a negative
coefficient. When the estimate of ECt is 1, 100% of
the adjustment occurs during the period or is
immediate and complete. When the estimate of ECt
is 0.5, 50% of the adjustment occurs throughout
each period or year. It is no longer logical to assert
that there is a long-run connection when ECt = 0,
which demonstrates that there has been no
adjustment, [18].
The selected model of ARDL by using (the
SIC) criterion to determine the lags gave significant
coefficient estimation results for the (DAI1(-2))
variable in the short run. On the other hand, the
selected model did not give significant coefficient
estimation results for all independent variables in
the long run as Table 7 shows. To ensure the
stability of the model, we refer to Cumulative Sum
(CUSUM) as well as Cumulative Sum of Squares
(CUSUMSQ) graphs (Figure 3). Since the two
diagrams in Figure 3 show that all the plotted
points were between the two red-colored bounds
that mean the used model was stable.
A. ARDL Model Estimation in the short run and long run
Table 7. ARDL Model Estimation in the short run and long run
Variable
Coefficient
Std. Error
t-Statistic
Prob.*
GDP_GROWTH1(-2)
-0.851580
0.066602
-12.78606
0.0000
KT1(-2)
-0.282575
0.039887
-7.084353
0.0000
LT1(-2)
2.221167
0.223161
9.953204
0.0000
TRADE_RATIO1(-2)
0.492708
0.036914
13.34735
0.0000
DAI1(-2)
0.200510
0.022195
9.033895
0.0000
GET1(-2)
0.075727
0.034485
2.195931
0.0318
C
-0.002684
0.001644
-1.632789
0.1075
R-squared
0.999988
Mean dependent var
0.008527
Adjusted R-squared
0.999984
S.D. dependent var
0.438440
S.E. of regression
0.001731
Akaike info criterion
-9.686622
Sum squared resid
0.000189
Schwarz criterion
-9.154522
Log-likelihood
410.3082
Hannan-Quinn criter.
-9.473137
F-statistic
301754.0
Durbin-Watson stat
2.073334
Prob(F-statistic)
0.000000
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Fig. 3: Cumulative Sum (CUSUM) and Cumulative Sum of Squares (CUSUMQ)
4.4.5 The Results of the Estimated Model in the
Short Run
The analysis of short-run and long-run relationships
is based on the result in Table 8. Estimates indicate
in the short run that all independent variables have
a significant influence on the growth of the GDP in
Saudi Arabia. In detail, the variables the human
capital, the digital adoption index, and the
Government expenditure per GDP have significant
positive impacts on the dependent variable GDP
growth. On the other hand, the variables and the
total invested capital have a significant negative
impact on the dependent variable GDP growth. In
detail, whenever the digital adoption index
increases by one unit, the GDP Growth increases
by approximately (20%).
B. ARDL Model Estimation in the short run and long run using error correction
Table 8. ARDL Model Estimation in the short run and long run using error correction
Variable
Coefficient
Std. Error
t-Statistic
Prob.*
GDP_GROWTH1(-2)
-1.034681
0.029837
-34.67733
0.0000
KT1(-2)
-0.297741
0.015404
-19.32833
0.0000
LT1(-2)
2.476733
0.090698
27.30756
0.0000
TRADE_RATIO1(-2)
0.579563
0.016479
35.16976
0.0000
DAI1(-2)
0.255272
0.009204
27.73612
0.0000
GET1(-2)
0.055870
0.012639
4.420278
0.0000
DEQ1(-2)
0.242386
0.038474
6.300074
0.0000
C
-0.002504
0.000760
-3.296092
0.0017
R-squared
0.999999
Mean dependent var
0.013681
Adjusted R-squared
0.999998
S.D. dependent var
0.446058
S.E. of regression
0.000627
Akaike info criterion
-11.68494
Sum squared resid
2.24E-05
Schwarz criterion
-11.05044
Log-likelihood
476.7126
Hannan-Quinn criter.
-11.43094
F-statistic
1945590.
Durbin-Watson stat
0.875527
Prob(F-statistic)
0.000000
Long run estimates
Variable
Coefficient
Std. Error
t-Statistic
Prob.
KT1
-0.223983
0.119612
-1.872586
0.0663
LT1
1.345285
0.449493
2.992893
0.0041
TRADE_RATIO1
0.419437
0.049108
8.541074
0.0000
DAI1
0.254884
0.030620
8.324138
0.0000
GET1
-0.167027
0.083341
-2.004133
0.0498
DEQ1
136.0574
34.65313
3.926266
0.0002
C
-0.213499
0.030095
-7.094071
0.0000
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4.4.6 The Results of the Estimated Model in the
Long Run
Estimates of long-term results reveal that there is a
statistically significant relationship for all
independent variables.
4.4.7 Short-run Relationship Estimation Results
Estimates of Short-run results in Table 8, detect
that there is a statistically significant and non-
negative economic link for the variable, this means
that the relationship is positive in the short-term.
Estimates indicate in the short run that total
invested capital significantly affects the GDP
growth in Saudi Arabia, and the parameter of total
invested capital is negative, which means that there
is an indirect relationship between gross domestic
product and total invested capital. On the other
hand, human capital, digital adoption index,
Government expenditure per GDP, and trade ratio
per GDP affect the GDP growth in Saudi Arabia,
and the parameters of these variables are positive,
which means that there is a direct relationship
between gross domestic product and these
variables. In detail, Whenever the digital adoption
index increases by one unit, the GDP Growth
increases by approximately (5%), About the results
of the error correction model (ECM), Table 8. This
shows that the error correction term (DEQ1(-2)) is
highly significant at the specified level of
significance, 5%, this indicates the existence of a
long-term equilibrium relationship cointegration
relationship among the model variables. The
coefficient of (DEQ1(-2)) approximately equals
(0.24). This means that deviations in the short-run
are corrected by approximately 24% within one
year towards the long-run equilibrium relationship.
4.4.8 Long-run Relationship Estimation Results
Estimates of long-term results shown in Table 8,
reveal that there is a statistically significant and
positive economic relationship between human
capital, digital adoption index, Government
expenditure per GDP, and trade ratio per GDP
affects the GDP growth in Saudi Arabia, and the
parameters of these variables are positive, which
means that there is a direct relationship between
gross domestic product and these variables. In
detail, Whenever the digital adoption index
increases by one unit, the GDP Growth increases
by approximately (25%).
4.4.9 Bounds Test
Table 9, shows the existence of the covariance
relationship (long-run relationships) in the (ARDL)
model, the bounds test is used, and the significance
of this test is recognized by its F-Statistic value,
[18]. Since the computed (F-statistic) value is
smaller than the lower bound, I (0), of the critical
values at the 1% significance level, the result
suggests that there is no cointegration connection.
The absence of a long-run equilibrium connection,
or the null hypothesis H0, which claims that there
is no cointegration, is thus accepted.
4.4.10 ARDL Diagnostic Tests
The following table shows the diagnostic tests of
residual distribution, autocorrelation, and
identification problems. Going to Table 12, the
results indicate that: 1) the residuals of this model
are normally distributed lines Figure 3, [21], [23],
where H0 is accepted from the Jarque-Bera
statistic because the corresponding p-value is
greater than 5 % significance level. 2) there is no
serial correlation, [21], [24], where the H0
acceptance from the Breusch-Godfrey (BG) test for
LM serial correlation (Autocorrelations) is accepted
because the corresponding p-value is greater than
5%. 3) H0 acceptance of the Ramsey RESET test
(regression specification error test) to detect
general functional form misspecification, [20]. But
regarding the heteroscedasticity, [21], it failed to
reject H0 from the Breusch-Pagan-Godfrey (BPG)
test as Table 10, depicts.
Table 9. Bounds Test Result
F-Bounds Test
Null Hypothesis: No levels of relationship
Test Statistic
Value
Signif.
I (0)
I (1)
F-statistic
1.477730
10%
2.08
3
K
5
5%
2.39
3.38
2.5%
2.7
3.73
1%
3.06
4.15
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Table 10. ARDL Diagnostic Tests Result
Residuals Distributed
Normality Test (Jarque-Bera)
0.627
0.731
Serial Correlation
LM Test/ Breusch-Godfrey (BG)
0.281639
0.7555
Heteroskedasticity
Breusch-Pagan-Godfrey Test (BPG)
0.842591
0.6399
Stability
Ramsey RESET Test
0.851254
0.3598
0
1
2
3
4
5
6
7
8
9
-1.5e-05 -1.0e-05 -5.0e-06 1.0e-11 5.0e-06 1.0e-05 1.5e-05
Series: Residuals
Sample 2015M06 2021M12
Observations 79
Mean -2.55e-18
Median 3.40e-07
Maximum 1.45e-05
Minimum -1.80e-05
Std. Dev. 7.00e-06
Skewness -0.197288
Kurtosis 2.813122
Jarque-Bera 0.627437
Probability 0.730725
Fig. 4: Jarque- Bera Normality Test
5 Results
5.1 Unit-root Test:
After considering the AIC criterion, the study
variables in the time series stationarity test (unit
root test) indicate that they are at level I (0) or at
the first difference, I (1), allowing us to use the
ARDL methodology to determine the short- and
long-term relationships between the variables.
5.2 Econometric Model
The growth model they suggest has the
following structure:
When, : represents the dependent variable
which is calculated by the formula.
when GDP is GDP per capita in the Kingdom of
Saudi Arabia. These elements will not promote
economic growth as they did in the past when
human capital and investment capital were fully
used. There will be a slowing or a decline in
economic growth. however, demonstrates that the
digital transition is essential for fostering economic
growth. It is concluded that the growth model,
which is represented as follows, should incorporate
the digital transformation variable () as well as
the government expenditure ( and the trade
openness ( ) World Bank database.
5.2.1 Optimal Lag Selection
To determine the ideal length of the ARDL model
variables, the (SIC) criterion with one lag period
was initially used. However, it did not produce
results of estimates that were symmetrical with the
variables and model of the study, so the (AIC)
criterion with the lowest value that was selected by
(2) lags was chosen to finish the study.
5.2.2 ARDL Approach
In this work, we are using the Bounds Testing
Approaches to the Analysis of Level Relationships
and Autoregressive Distributed-Lag (ARDL)
techniques, [22]. One of the newest dynamic
models that considers the element of time is the
ARDL model. We analyze long-run correlations
between variables based on time series data to
pinpoint the short- and long-term linkages between
the variables as well as the rate at which the system
will reach equilibrium. Two elements make up this
model: (1) Autoregressive (AR) models, which
employ the dependent variable as a lagged
independent variable, and (2) Distributed Lagged
(DL) models, which show that the dependent
variable also influences.
5.2.3 ARDL Model Estimation in the Short Run
and Long Run
The selected model of ARDL by using (the SIC)
criterion to determine the lags gave significant
coefficient estimation results for the (DAI1(-2))
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variable in the short run. On the other hand, the
selected model did not give significant coefficient
estimation results for all independent variables in
the long run.
5.2.4 Bounds Test:
According to the results in the table, the F-statistic
is equal to (1.477730) less than I (0) which is equal
to 3.06 at 1% Significantly, we accept H0, that
there is no cointegration between the independent
variables and the GDP GROWTH.
6 Discussion and Conclusion
The sample period covers the years from 2015 to
2022 (8 years) of the Saudi economy. All data used
for the variables of this study are secondary data
sourced from the World Bank's “World
Development Indicators (WDI) database.
Regarding the stands for the digital adoption index
of the Kingdom of Saudi Arabia, reports for
digitalization various years, [13]. The study data is
expanded to monthly data using methods of, [15],
[16].
Hypothesis 1 (H1): There is no relationship
between economic growth and digital transformers
in the energy industry.
The study results show that the Correlation
Matrix indicates that there is no multicollinearity
between independent variables as long as the
Pearson correlation coefficient is less than (0.8). In
the same context, there is a positive correlation
coefficient between economic growth variables in
KSA and the variable's Trade ratio, DAI. In detail,
there is a significant positive correlation between
the economic growth variable and digital
transformers in the energy industry with a
significant positive Pearson correlation coefficient
of 0.280050.
Hypothesis 1 (H2): There is no significant impact
of the digital transformers on the economic growth
in the energy industry.
Estimates indicate in the short run that all
independent variables have a significant influence
on the growth of the GDP in Saudi Arabia. In
detail, the variables the human capital, the digital
adoption index, and the Government expenditure
per GDP have significant positive impacts on the
dependent variable GDP growth. On the other
hand, the variables and the total invested capital
have a significant negative impact on the dependent
variable GDP growth.
Table 11. Coefficient of DAI in the long run and
short run with and without error correction
STATISTICS
Without error
correction
With error
correction
Short
run
Long
run
Short
run
Long
run
coefficient
0.201
0.112
0.056
0.25
T-test
9.03
0.504
4.42
8.324
p-value
0.000
0.0616
0.000
0.000
As the results in Table 11, the estimates of the
variables in the short and long run, it is noted that
the digital adoption index positive to the increase in
GDP growth in the long term with error correction
term while it did not give any effect on GDP
growth in the long term without error correction
term. On the other hand, it is noted that the digital
adoption index is positive to the increase in GDP
growth in the short term with error correction term
and without error correction term.
Estimates of long-term results reveal that there
is no statistically significant relationship for all
independent variables. According to the results in
the table, F-statistics equal to ( 1.477730) less than
I(0) which is equal to 3.06 at 1% Significant, We
accept H0, that there is no cointegration between
the independent variables and the GDP GROWTH.
Bounds Testing Approaches to the Analysis of
Level Relationships and Autoregressive
Distributed-Lag (ARDL) approaches are being used
in this study, [22]. The ARDL model is one of the
most recent dynamic models that take time into
account. To determine the short- and long-term
connections between the variables as well as the
pace at which the system will attain equilibrium,
we analyze long-run correlations between variables
based on time series data. This model consists of
two components: The dependent variable is used as
a lagged independent variable in (1) autoregressive
(AR) models, and (2) distributed lag (DL) models,
which demonstrate that the dependent variable also
impacts. The selected model of ARDL by using
(the SIC) criterion to determine the lags gave
significant coefficient estimation results for the
(DAI1(-2)) variable in the short run. On the other
hand, the selected model did not give significant
coefficient estimation results for all independent
variables in the long run.
Table 10 shows the diagnostic tests of residual
distribution, autocorrelation, and identification
problems. Returning to Table 11, the results
indicate that: First, the residuals of this model are
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normally distributed lines Figure 4, [20], [22],
where H0 is accepted from the Jarque-Bera
statistic because the corresponding p-value is
greater than 5% significance level. Second, there
is no serial correlation, [23], [20]. where the H0
acceptance from the Breusch-Godfrey (BG) test for
LM serial correlation (Autocorrelations) is accepted
because the corresponding p-value is greater than
5%. Third, H0 acceptance of the Ramsey RESET
test (regression specification error test) to detect
general functional form misspecification, [19]. But
regarding the heteroscedasticity, [20], it failed to
reject H0 from the Breusch-Pagan-Godfrey (BPG)
test.
Overall, digital transformation has the
potential to revolutionize the energy sector in Saudi
Arabia by improving efficiency, sustainability, and
resilience. However, challenges such as
cybersecurity risks and policy implementation need
to be addressed for the successful integration of
digital solutions into the respective industry.
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E-ISSN: 2224-2899
343
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Contribution of Individual Authors to the
Creation of a Scientific Article (Ghostwriting
Policy)
The authors equally contributed to the present
research, at all stages from the formulation of the
problem to the final findings and solution.
Sources of Funding for Research Presented in a
Scientific Article or Scientific Article Itself
This research was funded by the Deanship of
Scientific Research at KFU: Project No.
GRANT4,705.
Conflict of Interest
The authors have no conflicts of interest to declare.
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DOI: 10.37394/23207.2024.21.29
Yousif Osman, Isam Ellaythy, Yahya Daghri
E-ISSN: 2224-2899
344
Volume 21, 2024
