Digital Education Products:
Can Digital Education Products Affect GDP Growth?
AFAG HUSEYN1, ELNURA SHAFIZADE1, SUGRA HUMBATOVA2, TAHMASIB HUSEYNOV3
1Department of Business Administration, UNEC Business School,
Azerbaijan State University of Economics (UNEC),
Istiqlaliyyat str. 6, Baku,
AZERBAIJAN
2Department of Economics,
Azerbaijan State University of Economics (UNEC),
Istiqlaliyyat str. 6, Baku,
AZERBAIJAN
3International Center for Graduate Education,
Azerbaijan State University of Economics (UNEC),
Istiqlaliyyat str. 6, Baku,
AZERBAIJAN
Abstract: - The widespread use of new information and communication technologies in education has changed
the nature of the general economic environment. There is an increase in digital educational resources all over
the world. However, what is the weight of the business of digital education products in GDP and how this
business affects GDP growth? The paucity of literature on the relationship between digital educational products
and GDP calls for a thorough study of this relationship. The article is devoted to an analysis of the relationship
between digital educational products and GDP. This is one of the problems with the development of the digital
economy. Our main research hypothesis is to find the level of connection between digital educational products
(resources) and GDP, as well as the level of influence of qualitative and quantitative factors on this connection.
It should be noted that the absence of many statistical data during the analysis made our work difficult, and
many factors are of a qualitative or fuzzy nature. Based on such indicators, econometric models are not suitable
for determining the dependence of a factor on other parameters. The study used the statistical data of
Azerbaijan for 2010-2020. The fuzzy output logic method was implemented in the MATLAB software
package. It was revealed that digital educational products affect the growth of the GDP and the balanced
development of the country. The approach proposed in this paper is that the digitalization of education and the
improvement of public education and technology policies should continue. We believe that in connection with
the growth of the digital education market, the State Statistics Committee will need to generate specific data on
digital educational products in the future.
Key-Words: - Digital economy, Digital educational, GDP, Development, Education and technology policy,
Fuzzy Model
Received: April 14, 2023. Revised: September 22, 2023. Accepted: October 1, 2023. Published: October 13, 2023.
1 Introduction
Our research shows that in recent decades, the world
economic system has taken on a new look that is
associated with virtual and intangible factors:
information, investment, technology, labor,
intellectual and financial resources, management
systems, political, and in some cases, even religious
processes, [1]. The fundamental changes that
characterize the new economic system require a
reassessment of hitherto known scientific theories
and approaches. Theoretical results and postulates
typical of industrial economy laws are unable to
explain a number of processes and situations of the
new economic system in the era of globalization,
and the emergence of new guidelines and priorities
places the state in charge of revising its economic
system and policies as well as selecting mechanisms
for implementing its directions.
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Afag Huseyn, Elnura Shafizade,
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The globalization of the world economy is
primarily characterized by the emergence of
qualitative changes in the real sector of the economy
and the infrastructure sector.
Preference is given to the production of high-
tech and science-intensive products as the final
product, which gradually creates conditions for
strengthening the exchange of technologies in
international trade.
The information capacity of products is
increasing, and more attention is being paid to their
quality indicators. For example, in computers, the
cost of materials is only 2% of the cost of
microprocessors, and the cost of information
components in expensive cars is 2/3 of their price.
Hence, it becomes clear that the importance of raw
materials in foreign trade is gradually declining. The
fact that high technology is the monopoly of
industrialized countries and transnational
corporations is a negative situation. That is, there
are changes in the system of the world economic
hierarchy, which, as a result, shows that the
processes of monopolization in the modern world
economic system, in part, exist in technology,
modern education, fundamental and applied science,
the media, and highly qualified personnel. Speaking
of industrialized countries, I would like to note that
it would be more accurate to call such countries
more technologically and informationally advanced.
Because the proportion of people employed in these
countries is only 20% of industry, and the rest are
employed in the service and technology sectors, [2].
As a result, these countries, becoming centers of
income from these technologies, and also centers of
new applied technologies, attract potential scientific
and technical personnel. As a result, countries
deprived of such opportunities face both a shortage
of specialists and a situation where "brains and
intellects" leave their countries. Another question is
whether the development of ICT and the mass use
of "online" communication have changed the
structure of "space-time-matter", that is, the essence
of existence. Thus, in society and its development,
changes began to occur. Large masses of people
were able to reduce time and expand space by
sharing "everyone with everyone" (peer-to-peer). In
economic terms, such a situation led to the
appearance of virtuality. Loyalty not only covers
production spheres related to trade, but also plays a
role in increasing the independence of the spheres of
knowledge and education and the capital invested in
them.
Traditional mass media are replaced by
websites, and new information carriers are created.
They also develop network platforms, forming a
new economic environment, and as a result, global
networks of economic influence are created, [3]. For
example, Google, Facebook, etc. They collect
information about wishes and aspirations, demand
characteristics, and participate in its formation,
creating a database and influencing the creation of a
new economic environment. Thus, world economic
processes have a new volume of information, and
the current vector of changes even sometimes
becomes unpredictable. This means that the theories
and laws that have previously worked in the real
economy may no longer be able to fully explain and
operate in a variety of processes. That is, new laws
for the new economic system are formed. It is clear
that the analysis of changes in the real sector is not
enough for the analysis of the virtual economy.
However, the system of indicators for the virtual
economy has not yet been fully formed, and
research in this area continues, [4]. Turning to
theory, it should be noted that modern economic
growth, which is the main indicator, is perceived
when the GDP growth rate constantly exceeds the
population growth rate. This was seen as a new
phenomenon in the history of the world and the
economy. On the other hand, the law of diminishing
returns in traditional economic theory can no longer
explain a number of processes in the modern era and
does not apply to them. That is, as information
products spread, in volume and scale, they generate
more income, and this income opportunity can be
applied in the long term.
Despite the high fixed costs and low variable
costs of an information product, the original product
is expensive to create, but as the scale of production
increases, re-production becomes cheap. This leads
to a sharp increase in the intensity of the income
generated due to the increasing scale of the virtual
economy. But “because so many of the internet
services and digital products we each use are free,
they largely go uncounted in official measures of
economic activity such as GDP and productivity
(which is simply GDP/hours worked). The
contribution of the information sector as a fraction
of the total GDP has barely changed over the past 40
years (hovering between 4-5% and reaching a high
of only 5.5% at the end of 2018). The reason is that
GDP is based on what people pay for goods and
services, so if something has zero price, then it has
zero weight in GDP”, [4]. This problem is growing
every year, and so far, digital educational products
have not fully reflected in GDP growth.
The expansion of digital data flows is important
for the achievement of virtually all the Sustainable
Development Goals. The COVID-19 pandemic had
a dramatic impact on Internet traffic, as most
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Afag Huseyn, Elnura Shafizade,
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activities increasingly took place online. Against
this backdrop, global Internet bandwidth rose by 35
percent in 2020, the largest one-year increase since
2013. Monthly global data traffic is expected to
surge from 230 exabytes in 2020 to 780 exabytes by
2026, [5].
Digital education is defined as an inventive
method of using digital technologies and tools that
is carried out during the whole process of teaching
and learning. Post-pandemic growth in investment
in education technology is linked to the recent rapid
“digitization” and “identification”, [6], [7]. The
global digital education market is expected to be
valued at USD 77.23 billion by 2028 and to grow at
a compound annual growth rate (CAGR) of 30.5%
during the forecast period, [8]. The global online
education market promises to reach $282.62 billion
by 2023. According to Global Market Insights, in
2017, it was measured at $159 billion; in 2018, it
was $190 billion; and in 2019, it was $205 billion.
Its average annual rate of growth in the next 5–7
years, according to various forecasts, will be 7–10%
(in global reports, they take an average figure,
adjusted for the fact that the growth of the industry
is uneven), [9].
In the digital economy, the focus is on owning
and controlling digital assets with permanent future
economic benefits, [7], [10]. What matters is the
ownership and control of digital platforms, shared
content, digital user data collected, and associated
copying or intellectual property rights. We
understand that educational technology is embedded
in broader shifts in the global digital economy,
where resources, services, or data sources that can
be created as property bring future and permanent
economic benefits through forms of economic rent,
[7], [11]. Intellectual property in digital education
can be classified as “returnable income”. potential
as “expected future cash flows”, [7], [12].
As we can see, the digital education market is
constantly growing, and the volume of digital
educational products is growing along with it, which
are often improved. Our main research hypothesis
postulates a link between digital educational
products and GDP. An increase in the number of
digital educational products may increase their
weight in the structure of GDP, which may
eventually affect its growth. Based on this, we set
ourselves the task of identifying the relationship
between digital educational products and GDP. It
has been determined that the literature on the
relationship between digital educational products
(resources) and GDP is insufficient, and a
comprehensive study of this relationship has not
been conducted. Our main research hypothesis is to
find the level of connection between digital
educational products (resources) and GDP, as well
as the level of influence of qualitative and
quantitative factors on this connection.
The article is structured as follows: In the
second section, we outline digital education and
digital educational products. In the third section, we
review the literature and point out existing research
gaps. After that, we outline the missing statistics and
research methods and the construction of logical
rules based on expert reasoning. This section is
followed by the results obtained. In the final section,
we give conclusions, directions for future research,
and a list of used literature.
2 Digital Educational
Changes in the world and capital embodied in
market knowledge are valued more than capital
reflected in material form. This reflects the
formation of a new world, a new society, a new
system of relations, a completely different way of
thinking and principles of interaction, and a
knowledge economy with high dynamism in time
and space. Let's note one point. But what is meant
by the knowledge economy? It should be noted that
it is frequently associated with the information and
network economies, not separately, but all together
in a number of works of literature, [13], [14], [15],
[16], [17]. But there are principles that the
knowledge economy itself refers to. Basically, this
is the presence of a person's internal potential,
abilities, talent, and non-standard approach to
various processes and events. In a nutshell, these are
principles formed on the basis of a person's
psychological, spiritual, and socioeconomic values.
Today, not only innovations but also a person's, his
leading role in economic growth and increasing the
competitiveness of the country are becoming more
and more discussed, [18], [19], [20], [21]. The sharp
increase in the exchange of information and the
acceleration of economic dynamism ultimately
create a demand for increased knowledge. To
achieve the transition to a low-carbon and resource-
saving economy, we need to introduce new tools,
technologies, products, and production models
through education, [22]. Considering that only
knowledge creates more knowledge, its increase can
lead to the enrichment of intellectual resources and
increase their productivity. In other words, the
acquisition of knowledge does not reduce them as a
resources, but, on the contrary, increases them.
Thus, this process spreads from one person to the
whole society. We can say that the dissemination of
knowledge is limited to the use of patents and
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licenses as a result of intellectual creativity. The
predominance of the fourth-fifth technological
structure may lead to their lagging behind in the
competitive struggle and the loss of their positions
on the world market compared to countries that are
already using the advantages of the new
technological structure in the context of
globalization. On the other hand, the fact that
knowledge is a product, different from information
and ordinary resources, requires that it be treated not
only as a main resource and product but also as a
management tool. Therefore, in the new economic
model, the approach to this problem is changing, the
right to individual use of knowledge is limited, and
the opportunities for its use by society are gradually
increasing. At this time, it is necessary not to use a
person as a resource but to use his internal
capabilities to create conditions for the development
of personality and an external environment for self-
development and the perfection of moral values. At
that time, the Prime Minister of India, Jawaharlal
Nehru, pointed out in his book that “the very rapid
growth of technology and the practical application
of vast developments in scientific knowledge are
now changing the world picture with amazing
rapidity, leading to new problems”, [23]. New
technologies require new thinking. If we pay
attention to the experience of developed foreign
countries, we will see that this is an increase in the
creative and intellectual activity of a person. This
has already begun to manifest itself as one of the
defining competitive advantages and in some cases,
the most important one. Every day, a new class is
formed and grows—the creative class (strata). There
is a growing need to increase opportunities for
human creativity, intellectual activity, and the
expansion of opportunities for creativity. As a
result, in the conditions of globalization, the
formation of the ability to transition from creative
groups to a creative society and the use of "global
streams of talent" are required. This is possible with
the development of new methods, technologies,
transmitters of information, and means of
communication. After the pandemic, certain studies
explained how sustainable development goals
represent the interrelationship between digitalization
and sustainability, [24]. Building digital networks,
business managers, and policymakers using digital
means can create some unique opportunities to
strategically address sustainable development
challenges for the United Nations Targets (SDG) to
ensure higher productivity, better education, and an
equality-oriented society. The idea of data-driven
governance introduced in the 2030 Agenda for
Sustainable Development emphasizes the need to
“significantly increase the availability of high-
quality, timely, reliable, and disaggregated data by
2030”, [24], [25]. Digital educational resources play
a special role in this process.
It is clear that the use of electronic resources in
the educational process creates a number of
opportunities. The presentation of educational
materials in digital format allows you to conduct
lessons more effectively and check the level of
knowledge interactively; more complex experiments
do not require additional resources. The government
has taken a number of steps to expand the use of
electronic resources in the educational process.
"Program of Provision of General Education
Schools of the Republic of Azerbaijan with
Information and Communication Technologies for
2005-2007" was adopted. Within the framework of
the state program, 37.9 thousand copies of 20 names
of the history of Azerbaijan, 15, 160 million copies
of electronic textbooks on eight names of chemistry,
22, 740 million copies of 12 names of electronic
textbooks on biology, 10, 2 thousand copies of the
history of Azerbaijan, etc. were prepared.
Since the adoption of the "State Program of
Informatization of the Education System of the
Republic of Azerbaijan for 2008-2012", the creation
and use of electronic resources in the educational
process have expanded even more. As part of this
state program, 122 electronic lessons on 8 subjects
(physics, chemistry, biology, geography, literature,
mathematics, computer science, and music) are
available on the National Educational Portal
(www.edu.az). On the portal there are such
electronic resources as "The message of the heritage
of Azerbaijan", "The virtual museum of Uzeir
Hajibekov", "Living voices of the history of
Azerbaijan", “Language Learning Inserts” and
“Learning English”. Also, as an example, you can
give INFOKO resources for primary classes. To
evaluate the quality of electronic resources, the
Ministry of Education created the Council for the
Evaluation of Electronic Educational Resources.
In addition, the electronic learning platform
Intel SKOOOL is adapted to the education system
of Azerbaijan, and 400 electronic lessons are
adapted to the portal www.skoool.edu.az. As a
result, an archive of more than 2000 electronic
resources was formed in 2010-2012.
Due to the COVID-19 pandemic, mainly since
2020, electronic lessons and textbooks have become
the basis of educational resources in the education
system of Azerbaijan.
Along with these positive changes, there are
also many problems. The main problems can be
formulated in four groups:
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1. Obstacles on the way to increasing the share of
intangible assets in GDP
2. Problems with defining and protecting
intangible asset property rights
3. Low operability of information for authors
4. Difficulties of adapting to the requirements of a
new technological system
We will focus our study on the analysis of the
relationship between GDP and digital educational
products (resources), paying more attention to the
problem of group I.
To do this, we conducted a series of analyses.
3 Literature Review
The following section provides a brief overview of
the existing literature on and related to the
relationship between digital education and GDP.
The rest of the article is structured as follows:
Section 4 discusses the data and methodology used
in our analysis, including any limitations. Section 5
presents the results of our model, and Section 6
discusses these results and their implications for our
research hypotheses. Finally, the final section
summarizes the main findings and provides
direction.
3.1 Relationship between Digital Education
and GDP, Economic Development
The difference between our article and other articles
of this type is that there is a direct link between
digital education and GDP. The main difference
between the current study and the available
literature is as follows: this article examines the
impact of both qualitative and quantitative
indicators on GDP. Such quantitative indicators as
the weight of products (resources) of digital
education in GDP, the use of web pages (Internet
portals), software development, the number of
products (services) in the field of ICT, Internet
communication, and the number of employees
working in the field of ICT in many countries attach
great importance to determining the level of
development of the information society. However,
we assume that the development of the digital
educational market is particularly influenced by the
quality of the digital educational product. Because
customer satisfaction is of great importance in the
reuse and distribution of this product, this affects the
volume of their sales, which can directly affect the
growth of the GDP. Therefore, unlike other studies,
we consider the impact of such qualitative indicators
as the flexibility of use in all specialty profiles and
satisfaction with product quality. Using these
indicators for analysis can give an idea of the
potential for GDP growth in countries where digital
education exists. Our study will help determine the
level of influence of the flexibility of using digital
products in all specialty profiles and satisfaction
with the quality of digital educational products on
GDP. We believe that by stimulating an increase in
the quality and flexibility of the use of educational
products, it is possible to achieve not only the
development of the information society but also the
growth of the GDP. The literature reviewed by us
showed that there are no studies on the impact of the
above qualitative and quantitative indicators on
GDP using the fuzzy logic method.
Studies mainly cover the impact of education on
GDP and economic development, [26], [27], [28],
[29], [30], [31], [32], [33], [34]. We started the
literature review with general studies and presented
them in the following order: Using panel data from
2000 to 2014 from the education and technology
sectors in 53 countries, discovered and assessed the
relationship between education and technology
efficiency and national competitiveness, national
balanced development, national energy efficiency,
exports, employment, and also nationwide macro-
and micro-development, [35]. It has been
determined that educational and technological
efficiency contribute to overall development to
varying degrees, depending on the dynamics of
economic development and the direction of
educational and technological policy, and the effect
has been assessed. It has been determined that
educational and technological efficiency contribute
to overall development to varying degrees,
depending on the dynamics of economic
development and the direction of educational and
technological policy. Here, for the first time,
efficiency was used as a driving force to measure
the impact on a country's development. We also
used this as a driving force in our study to measure
the impact of digital educational products
(resources) on GDP. According to Lazović et al., the
most important factor influencing GDP growth in
middle-income countries is investment in education,
while infrastructure plays a leading role in economic
growth in high-income countries, [36]. The most
important factor is education, since the coefficient
of influence of this factor is positive and has a
maximum value. Thus, an increase in all educational
variables with a positive sign, such as government
spending on education, adult and youth literacy
rates, and the population, leads to faster economic
growth. [37], examined the relationship between
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education, digitalization, and financial development
between 1996 and 2019 using the BVAR model to
demonstrate the differences between developed and
emerging economies in Europe and concluded that
financial development, including its two main
components, financial institutions and financial
markets, demonstrate the dynamic interdependence
between digitalization and education. In addition,
education was also identified as the leading variable
in the financial development, education, and
digitalization package. They also note that there are
corresponding geographic differences between
Western, more developed, and Eastern, emerging
economies, and that while developed countries are
approaching their full potential in terms of levels of
digitalization, education, and financial development,
developing countries still have significant potential
for growth, as the results in, [36], confirm above,
highlighting that many developing European
countries have experienced a high level of
digitalization in recent years but less progress in
education and financial development. In addition,
[38], considered education as important among
many factors influencing the poverty rate, which is
the main macroeconomic indicator of economic
development and economic growth, and
quantitatively analyzed the relationship between the
average year of schooling and per capita gross
domestic product income and found that this
relationship was significant in China and a positive
correlation was found. [39], proved the importance
of the digital transformation of education and higher
education institutions for sustainable development
on a scientific and practical basis. In, [40], they
conducted a systematic review of articles related to
innovative approaches to the sustainability of digital
education in their research and sought answers to
questions such as “What are the main challenges of
sustainability in digital education and how to
overcome these challenges associated with
educational innovation?”.
Barro found a positive relationship between
years of schooling and economic growth in almost
100 countries from 1960 to 1995, [26]. [41],
examined the relationship between investment in
ICT, education, and health in five West African
countries and noted that additional investment in
health and education beyond ICT itself has a
significant impact on human development. [34],
examined the impact of both secondary and tertiary
education on economic growth in Spain over the
period 1971–2013, highlighting their importance for
economic growth. In, [33], were able to shed light
on the contention that secondary and tertiary
education played a central and important role in
economic growth in India between 1975 and 2013,
between 2016 primary education. [42], examined
whether education contributes to economic growth
in 45 sub-Saharan African countries from 1993 to
2015 and concluded that the Internet has a positive
effect on economic growth in countries with better
access to education. In addition, [43], studied the
impact of tertiary education on gross domestic
product (GDP) per capita in EU regions and found
that the most favorable relationship between GDP
per capita and tertiary education was in Central and
Northern Europe and Ireland, while in southern
Russia as well as in some regions of Eastern Europe,
this relationship was determined to be weak.
3.2 Application of Fuzzy Logic in Economic
Processes
Fuzzy logic is widely used in modeling various
types of economic processes. Such as in, [44],
which applied fuzzy logic inference rules to a model
for optimizing the production and sectoral structure
of agriculture to ensure food security. [45], applied
fuzzy logic to profit optimization in virtual
businesses. In, [46], attention is paid to risk
assessment optimization for decision support using
an intelligent model based on fuzzy inference and
renewable rules. In their research, [47], they assess
the stability of the banking system based on fuzzy
logic methods. In, [48], investigate a decision-
making model based on fuzzy inference to predict
the impact of SCOR® indicators on customer
perceived value. In their approaches, [49], review
using fuzzy inference systems for the creation of
forex market predictive models.
Research shows that the relationship between
GDP and digital educational products is
understudied. There is a strong need to use
mathematical modeling to measure the impact of
digital educational products on GDP. Such
mathematical models allow to predict GDP
depending on the volume of digital educational
products.
Fuzzy logic is one of the most widely used
direction in mathematical modeling of economic
processes. This is due to the nature of the economic
system and processes. In the article, fuzzy logic
inference method was used to measure the impact of
digital educational products on GDP. The reasons of
this are further explained in the next section.
4 Materials and Methods
It should be noted that the absence of many
statistical data (the volume of digital educational
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products produced in piece or total terms; sales of
these products; frequency of use of these products,
the cost of digital educational products; the number
of personnel involved in the development of a
digital educational product, and others) during the
analysis made our work difficult, and many factors
are of a qualitative or fuzzy nature. Based on such
indicators, econometric models are not suitable for
determining the dependence of a factor on other
parameters. Various mathematical methods are used
to work with qualitative or fuzzy indicators. The
method of fuzzy inference systems (FIS) is one of
them, [44], [45], [46], [47], [48], [49], [50], [51],
[52], [53], [54], [55], [56], [57], [58]. This system
was proposed in 1975 by Ebhasim Mamdani.
Basically, it was anticipated to control a steam
engine and boiler combination by synthesizing a set
of fuzzy rules obtained from people working on the
system. This method allows you to determine the
dependence using both quantitative and qualitative
indicators, as well as fuzzy indicators. Therefore, in
our study, we use the method of fuzzy inference
systems. In the analysis, we used data that is
publicly available to the State Statistics Committee
and is shown in Table 1.
— The decision making block: Performs an
operation on the rules.
— Fuzzification Interface block: It converts crisp
quantities into fuzzy quantities.
— Defuzzification Interface block: It converts
fuzzy quantities into crisp quantities.
The block diagram of the fuzzy inference
system is given in Figure 1.
We’ll apply a fuzzy inference system for
defining the weight of digital education products
(resources) in GDP, [44], [45], [46], [47], [48], [49],
[56], [57], [58].
Functional blocks of fuzzy inference systems
(FIS) are, [44], [45], [50], [51], [52]:
— Rule Base: It contains fuzzy IF-THEN rules.
— Database: This block defines the membership
functions of fuzzy sets used in fuzzy rules.
Fig. 1: Block diagram of the fuzzy inference system
Source: FuzzyLogic.pdf (northeastern.edu)
Table 1. Statistical data of Azerbaijan in the 2010-2020 years. (stat.com.az)
Years
Usage of
web pages
(internet
portals)
(1000 USD)
Software
development
(1000 USD)
Internet
communication
(1000 USD)
Number of
employees
working in the
ICT sector,
(1000 person)
2010
0.250657977
3166.186239
51678.15516
17,3
2011
86.33184997
6170.120788
78570.88366
17,5
2012
193.5031847
6206.496815
109969.4268
17,6
2013
181.6443595
12814.27661
125469.7259
17,9
2014
792.7078021
16886.28251
141361.5502
18,0
2015
502.3085802
11122.86777
72151.98153
18,3
2016
1177.443949
12743.32185
67379.5674
18,5
2017
1155.932004
19135.6979
78049.5265
19,0
2018
1257.808364
24364.56679
85477.32486
19,3
2019
2492.912182
39789.77707
92407.50544
19,9
2020
2234.221516
41509.20534
117161.3435
20,1
Source: https://www.stat.gov.az/source/information_society/ (01.11.2022)
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Afag Huseyn, Elnura Shafizade,
Sugra Humbatova, Tahmasib Huseynov
E-ISSN: 2224-2899
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Steps for computing the output by the method FIS
are as follows, [50], [51], [52]:
Step 1. determining a set of fuzzy rules;
Step 2. fuzzifying the inputs using the input
membership functions;
Step 3. combining the fuzzified inputs according to
the fuzzy rules to establish a rule strength;
Step 4. finding the consequence of the rule by
combining the rule strength and the output
membership function;
Step 5. combining the consequences to get an output
distribution;
Step 6. defuzzifying the output distribution (this step
is only needed if a crisp output (class) is
needed).
So, first, we define output and input linguistic
variables. So, linguistic variables are: weight of
digital education products (resources) in GDP;
usage of web pages (internet portals); software
development, amount of products (services) in the
ICT sector; Internet communication, number of
employees working in the ICT sector; flexibility of
use in all specialty profiles; satisfaction with
product quality. Input variables for them are: usage
of web pages (internet portals), software
development, amount of products (services) in the
ICT sector, Internet communication, number of
employees working in the ICT sector, flexibility of
use in all specialty profiles, satisfaction with product
quality; and output variables are: weight of digital
education products (resources) in GDP. Suppose we
denote these linguistic variables, such as:
— weight of digital education products (resources)
in GDP -
— Usage of web pages (internet portals)-
— Software development - ;
— Amount of products (services) in the ICT sector
- ;
— Internet communication -
— Number of employees working in the ICT sector
-
— Flexibility of use in all specialty profiles-
— Satisfaction with product quality -.
Table 2 shows the term sets (bad, middle, and
good) for these variables.
The interval values of these variables,
corresponding to their term sets, are given in Table
3. For defining interval values of the weight of
digital education products (resources) in GDP,
statistical data from word practice was used. All this
data is given in percent, and the minimal value of
this parameter is 0%, but the maximal is 15%. So,
the interval values of the weight of digital education
products (resources) in GDP will be [0-15] (in
percent).
The linguistic variables (flexibility of using
in all specialty profiles) and (satisfaction with
product quality) are quality parameters. The interval
values of these variables were estimated based on
expert assessment as [0-10], [48], [55].
Table 2. Term sets of input and output linguistic variables
Linguistic variables
Variables
Term sets
Output variables
weight of digital education products (resources) in GDP, (in
percent)
y
bad
middle
good
İnput variables
Usage of web pages (internet portals) (1000 USD)
bad
middle
good
Software development (1000 USD)
bad
middle
good
Amount of products (services) in the ICT sector (mln USD)
bad
middle
good
Internet communication (1000 USD)
bad
middle
good
Number of employees working in the ICT sector, thousands
of people
bad
middle
good
Flexibility of use in all specialty profiles
bad
middle
good
Satisfaction with product quality
bad
middle
good
Source: Developed by the authors in the MATLAB program
WSEAS TRANSACTIONS on BUSINESS and ECONOMICS
DOI: 10.37394/23207.2023.20.193
Afag Huseyn, Elnura Shafizade,
Sugra Humbatova, Tahmasib Huseynov
E-ISSN: 2224-2899
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Volume 20, 2023
Table 3. Interval values of input and output variables corresponding to their term sets
Variables
Term sets
bad
middle
good
Output variables
y
[0-5]
[5-10]
[10-15]
[0.25-831.1378325]
[831.1378325-1662.025007]
[1662.025007-2492.92]
[3166.18-15947.19261]
[15947.19261-28728.19897]
[28728.19897-41509.21]
[878.6-1256.158455]
[1256.158455-1633.624868]
[1633.624868-2011.1]
[51678.16-81572.62018]
[81572.62018-111467.0852]
[111467.0852-141361.55]
[17-20]
[18-19]
[19-20]
[0-3]
[3-6]
[6-10]
[0-3]
[3-6]
[6-10]
Source: Developed by the authors in the MATLAB program
Then, it is implemented using fuzzy sets for
fuzzing. The membership function for these fuzzy
sets is constructed as a Gaussian function.
The next step is to construct logical rules on the
base of expert reasoning. For example, expert
reasoning can be written in the following form:
— If (usage of web pages is bad) and
(software development is bad) and (amount of
products (services) in the ICT sector is bad) and
(internet communication is bad) and (the number of
employees working in the ICT sector is bad) and
(flexibility of using in all specialty profiles is bad)
and (satisfaction with product quality is bad), then
(weight of digital education products (resources) in
GDP is bad);
— If (usage of web pages is middle) and
(software development is middle) and (amount of
products (services) in the ICT sector is middle) and
(internet communication is middle) and (the number
of employees working in the ICT sector is middle)
and (flexibility of using in all specialty profiles is
middle) and (satisfaction with product quality is
middle), then (weight of digital education products
(resources) in GDP is middle);
— If (usage of web pages is good) and
(software development is good) and (amount of
products (services) in the ICT sector is good) and
(Internet communication is good) and (the number
of employees working in the ICT sector is good) and
(flexibility of using in all specialty profiles is good)
and (satisfaction with product quality is god), then
(weight of digital education products (resources) in
GDP is good);
— If (usage of web pages is bad) and
(software development is middle) and (amount of
products (services) in the ICT sector is middle) and
(internet communication is middle) and (the number
of employees working in the ICT sector is middle)
and (flexibility of using in all specialty profiles is
middle) and (satisfaction with product quality is
middle), then (weight of digital education products
(resources) in GDP is middle);
— If (usage of web pages is bad) and
(software development is bad) and (amount of
products (services) in the ICT sector is middle) and
(internet communication is middle) and (the number
of employees working in the ICT sector is middle)
and (flexibility of using in all specialty profiles is
middle) and (satisfaction with product quality is
middle), then (weight of digital education products
(resources) in GDP is middle);
— If (usage of web pages is bad) and
(software development is bad) and (amount of
products (services) in the ICT sector is bad) and
(internet communication is middle) and (the number
of employees working in the ICT sector is middle)
and (flexibility of using in all specialty profiles is
middle) and (satisfaction with product quality is
middle), then (weight of digital education products
(resources) in GDP is middle);
— If (usage of web pages is bad) and
(software development is bad) and (amount of
products (services) in the ICT sector is bad) and
(internet communication is bad) and (the number of
employees working in the ICT sector is middle) and
(flexibility of using in all specialty profiles is
middle) and (satisfaction with product quality is
middle), then (weight of digital education products
(resources) in GDP is bad);
— If (usage of web pages is bad) and
(software development is bad) and (amount of
products (services) in the ICT sector is bad) and
(internet communication is bad) and (the number of
employees working in the ICT sector is bad) and
(flexibility of using in all specialty profiles is
middle) and (satisfaction with product quality is
middle), then (weight of digital education products
(resources) in GDP is bad);
— If (usage of web pages is bad) and
(software development is bad) and (amount of
products (services) in the ICT sector is bad) and
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DOI: 10.37394/23207.2023.20.193
Afag Huseyn, Elnura Shafizade,
Sugra Humbatova, Tahmasib Huseynov
E-ISSN: 2224-2899
2248
Volume 20, 2023
(internet communication is bad) and (the number of
employees working in the ICT sector is bad) and
(flexibility of using in all specialty profiles is bad)
and (satisfaction with product quality is middle),
then (weight of digital education products
(resources) in GDP is bad);
— If (usage of web pages is middle) and
(software development is bad) and (amount of
products (services) in the ICT sector is bad) and
(internet communication is bad) and (the number of
employees working in the ICT sector is bad) and
(flexibility of using in all specialty profiles is bad)
and (satisfaction with product quality is bad), then
(weight of digital education products (resources) in
GDP is bad);
— If (usage of web pages is middle) and
(software development is middle) and (amount of
products (services) in the ICT sector is bad) and
(internet communication is bad) and (the number of
employees working in the ICT sector is bad) and
(flexibility of using in all specialty profiles is bad)
and (satisfaction with product quality is bad), then
(weight of digital education products (resources) in
GDP is bad);
— If (usage of web pages is middle) and
(software development is middle) and (amount of
products (services) in the ICT sector is middle) and
(internet communication is bad) and (the number of
employees working in the ICT sector is bad) and
(flexibility of using in all specialty profiles is bad)
and (satisfaction with product quality is bad), then
(weight of digital education products (resources) in
GDP is bad), etc.
—
Then fuzzy inference logic rules will be in the
following form:
If (x1 is bad) and (x2 is bad) and (x3 is bad)
and (x4 is bad) and (x5 is bad) and (x6 is bad) and (x7
is bad), then (y is bad);
If (x1 is middle) and (x2 is middle) and (x3 is
middle) and (x4 is middle) and (x5 is middle) and (x6
is middle) and (x7 is middle), then (y is middle);
If (x1 is good) and (x2 is good) and (x3 is
good) and (x4 is good) and (x5 is good) and (x6 is
good) and (x7 is god), then (y is good);
If (x1 is bad) and (x2 is middle) and (x3 is
middle) and (x4 is middle) and (x5 is middle) and (x6
is middle) and (x7 is middle), then (y is middle);
If (x1 is bad) and (x2 is bad) and (x3 is middle)
and (x4 is middle) and (x5 is middle) and (x6 is
middle) and (x7 is middle), then (y is middle);
If (x1 is bad) and (x2 is bad) and (x3 is bad)
and (x4 is middle) and (x5 is middle) and (x6 is
middle) and (x7 is middle). then (y is middle);
If (x1 is bad) and (x2 is bad) and (x3 is bad)
and (x4 is bad) and (x5 is middle) and (x6 is middle)
and (x7 is middle), then (y is bad);
If (x1 is bad) and (x2 is bad) and (x3 is bad)
and (x4 is bad) and (x5 is bad) and (x6 is middle) and
(x7 is middle), then (y is bad);
If (x1 is bad) and (x2 is bad) and (x3 is bad)
and (x4 is bad) and (x5 is bad) and (x6 is bad) and (x7
is middle), then (y is bad);
If (x1 is middle) and (x2 is bad) and (x3 is bad)
and (x4 is bad) and (x5 is bad) and (x6 is bad) and (x7
is bad), then (y is bad);
If (x1 is middle) and (x2 is middle) and (x3 is
bad) and (x4 is bad) and (x5 is bad) and (x6 is bad)
and (x7 is bad), then (y is bad);
If (x1 is middle) and (x2 is middle) and (x3 is
middle) and (x4 is bad) and (x5 is bad) and (x6 is
bad) and (x7 is bad), then (y is bad) and etc.
5 Results
So the rules are constructed with the support of
linguistic variables for the weight of digital
education products (resources) in GDP.
Transforming the above rules, we'll get fuzzy sets
for the output variable at the base of each rule.
The composition method gives a fuzzy set, which is
the range of values of fuzzy output variables, and by
using the centroid method, we obtain a crisp
numerical solution.
A fuzzy inference logic method was realized by
the MATLAB software package, [56].
As the solution to this problem for each
linguistic variable, we obtain the following crisp
values:
If =2000 (*1000 USD) and =30000
(*1000 USD) and =1500 (mln USD) and
127225 (*1000 USD) and 19 (*1000
person) and 5 and 5, then .
The dependence of y (weight of digital
education products (resources) in GDP) on (usage
of web pages (internet portals)) and (satisfaction
with product quality) is shown in Figure 2.
WSEAS TRANSACTIONS on BUSINESS and ECONOMICS
DOI: 10.37394/23207.2023.20.193
Afag Huseyn, Elnura Shafizade,
Sugra Humbatova, Tahmasib Huseynov
E-ISSN: 2224-2899
2249
Volume 20, 2023
Fig. 2: Dependence of y (weight of digital education
products (resources) in GDP) on (usage of web
pages (internet portals) and (satisfaction with
product quality)
Source: Developed by the authors in the MATLAB
program
As the solution to this problem for each
linguistic variable, we obtain the following crisp
values:
If =2000 (*1000 USD) and =30000 (*1000
USD) and =1500 (mln USD) and 127225
(*1000 USD) and 19 (*1000 person) and
5 and 5, then .
İf usage of web pages (internet portals) is 2
mln.USD (2000 (*1000 USD)), software
development is 30 million USD (30000 (*1000
USD)), amount of products (services) in the ICT
sector is 1500 million USD, Internet communication
is 127.225 million USD (127225 (*1000 USD)),
number of employees working in the ICT sector is
19000 person, flexibility of using all specialty
profiles is 5, satisfaction with product quality is 5,
then weight of digital education products (resources)
in GDP is %.
This shows that upon reaching the average
quality of digital products and the average level of
flexibility in the use of digital educational products,
there will be a significant increase in GDP. But it is
also necessary to take into account that the
qualitative indicators vary from country to country.
Therefore, to ensure the necessary GDP growth
through the sale of digital educational products, it is
necessary to ensure a certain level above the
indicated quantitative indicators. Despite this, our
result showed that there is a clear relationship
between the selected quantitative and qualitative
indicators and GDP growth.
According to the results of this study,
manufacturers of digital educational products and
countries wishing to increase GDP through the sale
of these products should take into account the
motives of consumers. Since it is possible to adjust
the quality and quantity of the produced and sold
digital educational products, it is also possible to
determine changes in the level of GDP. This shows
that the changes that are taking place in the
information society by improving the accessibility,
quality, and volume of digital educational products
also have an impact on the development of the
digital economy. Therefore, we consider it
necessary to further analyze and discuss the impact
of qualitative and quantitative indicators of digital
educational products on GDP growth, depending on
the state of development of the digital economy.
6 Conclusion
It is no coincidence that the United Nations (UN)
has proclaimed the 21st century the “Century of
Education”, [59]. At present, due to the rapid
development of the Internet and digital technologies
in the global space, the requirements for the
education and formation of young people have
generally changed, both in Azerbaijan and around
the world. In the modern conditions of the formation
of the information society and the formation of a
knowledge-based economy, the development of ICT
has become one of the most important indicators of
the competitiveness, intellectual and scientific
potential of a country.
We can see that the result of our research is
similar to that of Xu and Liu, [35]. The
digitalization of education has a positive impact on
stability and development in both developed and
developing countries, increasing educational costs
and technological efficiency, and being an
inevitable choice for every country, both developed
and developing, regardless of the stage of
development.
The digital economy has five pillars: regulation,
infrastructure, network security, cybersecurity,
education, training, especially for the digital
economy, and building partnerships to create
backbone technology platforms. The issues of
education and its digitalization are important here,
[60].
Mass higher education in the 21st century is
more appropriate if it is predominantly distance
education, [61]. As we can see, the digital
education market is constantly growing, and the
volume of digital educational products is growing
along with it, which is often improved. Improving
education based on high-quality digital material can
increase the share of specialists in employment,
[62].
These results and the identified relationship
between digital educational products and GDP can
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Afag Huseyn, Elnura Shafizade,
Sugra Humbatova, Tahmasib Huseynov
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Volume 20, 2023
be used in the development of state programs for the
development of digital education.
We believe that, in connection with the growth
of the digital education market, the State Committee
on Statistics will in the future need to generate data
on the volume of digital educational products
produced in piece or total terms; sales of digital
educational products; frequency of use of these
products, the cost of digital educational products;
the number of personnel involved in the
development of a digital educational product, and
others. These indicators can make it possible to
analyze the development trend of one of the areas of
the digital economy, but also to identify the income
and motivation of producers of a digital educational
product, identifying not only the volume of these
products in GDP, but also revenues to the budget
from the taxation of these products. Therefore, we
believe that further research is needed in this
direction.
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Contribution of Individual Authors to the
Creation of a Scientific Article (Ghost-writing
Policy)
- Afag Huseyn was responsible for maintaining the
main body as well as conceptualization,
visualization, writing—original draft preparation,
writing—review and editing, supervision, and
project administration.
- Elnura Shafizade and Tahmasib Huseynov were
responsible for the methodology and carried out
the analysis.
- Sugra Humbatova was responsible for compiling
the manuscript, editing.
Sources of Funding for Research Presented in a
Scientific Article or Scientific Article Itself
UNEC Grant Number 1466/22/1DM7X1Z/Y/A1
Conflict of Interest
The authors have no conflicts 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
_US
WSEAS TRANSACTIONS on BUSINESS and ECONOMICS
DOI: 10.37394/23207.2023.20.193
Afag Huseyn, Elnura Shafizade,
Sugra Humbatova, Tahmasib Huseynov
E-ISSN: 2224-2899
2254
Volume 20, 2023