Access to this full-text is provided by MDPI.
Content available from Energies
This content is subject to copyright.
energies
Article
Developing Digital Economy and Society in the Light of the
Issue of Digital Convergence of the Markets in the European
Union Countries
Ryszard Borowiecki 1, Barbara Siuta-Tokarska 2, Jolanta Maro ´n 3, Marcin Suder 4, Agnieszka Thier 2
and Katarzyna ˙
Zmija 2, *
Citation: Borowiecki, R.;
Siuta-Tokarska, B.; Maro ´n, J.; Suder,
M.; Thier, A.; ˙
Zmija, K. Developing
Digital Economy and Society in the
Light of the Issue of Digital
Convergence of the Markets in the
European Union Countries. Energies
2021,14, 2717. https://doi.org/
10.3390/en14092717
Academic Editor:
Radoslaw Miskiewicz
Received: 24 March 2021
Accepted: 5 May 2021
Published: 10 May 2021
Publisher’s Note: MDPI stays neutral
with regard to jurisdictional claims in
published maps and institutional affil-
iations.
Copyright: © 2021 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
1WSB University in D ˛abrowa Górnicza, 41-300 D ˛abrowa Górnicza, Poland; borowier@uek.krakow.pl
2Department of Economics and Organization of Enterprises, Institute of Management,
Cracow University of Economics, 31-510 Krakow, Poland; siutab@uek.krakow.pl (B.S.-T.);
thiera@uek.krakow.pl (A.T.)
3Department of International Management, Institute of Management, Cracow University of Economics,
31-510 Krakow, Poland; zajacjol@uek.krakow.pl
4Department of Applications of Mathematics in Economics, Faculty of Management,
AGH University of Science and Technology, 30-067 Krakow, Poland; msuder@agh.edu.pl
*Correspondence: zmijak@uek.krakow.pl
Abstract:
The main objective of the paper is to present the development of digital economy and
society in the Countries of the European Union in the light of the digital convergence of these markets.
The analysis was based on the data on the Digital Economy and Society Index (DESI) from 2015 and
2020. Empirical studies have been conducted in two stages. First, basic measures of time series
dynamics were used to determine the dynamics of changes in index values. On the other hand,
in order to classify countries in terms of similarity of value and structure of the DESI, a cluster analysis
was used as one of the basic methods of the so-called unsupervised statistical grouping. The studies
presented confirm that the levels of indicators and the dynamics of changes in their value at the level
of the synthetic DESI indicator, and in the five underlying areas of analysis, vary despite the fact
that almost all are pro-growth, including for isolated clusters of EU-28 countries. When analyzing
the DESI synthetic index and its components, it was concluded that there is generally an increase in
convergence between the EU-28 countries in terms of the level of development of the digital economy
and society, as well as in four components: Connectivity, Human Capital, Use of Internet Services
and Digital Public Services. The Integration of Digital Technology area was the only one where
increasing polarization of results was observed. The added value of the paper is to fill the research
gap in research on the digital convergence of markets in the Countries of the European Union.
Keywords: convergence; digitalization; clusters; DESI
1. Introduction
The economy and the digital society are terms that are part of the European Union’s
‘Europe 2020’ strategy. This strategy is based on the pursuit of competitiveness and full
employment in the EU economy in a spirit of sustainability, as values ensuring equality of
intergenerational opportunities [
1
–
5
]. The development of such an economy and society is,
in the twenty-first century, indicated as the foundation of growth. The information and
communication technology (ICT) sector, which importance for development processes is ex-
pressed in its dynamic growth, plays a particular role in this regard, making it a significant
driver of economic, social and even civilizational progress, as some authors point out [
6
–
8
].
There is evidence that ICT positively affects the economy and social welfare in several
dimensions. It supports new types of interactions, services and business
practices [9–13]
,
accelerates the progress of various transactions, allows barriers to market access to be
overcome, eliminates spatial constraint, creates alternative commercial platforms and new
Energies 2021,14, 2717. https://doi.org/10.3390/en14092717 https://www.mdpi.com/journal/energies
Energies 2021,14, 2717 2 of 26
industries that generate new employment opportunities [
14
–
16
]. It changes the way peo-
ple interact with each other and obtain information [
17
]. Empirical studies show that
today the difference in countries’ economic performances and their global competitiveness
greatly depends on the level of acceptance, availability, and use of ICT [
18
–
21
]. However,
the development of ICT may create a digital divide between countries, regions, households,
individuals and companies in terms of access to and use of ICT [
22
]. To achieve benefits
from ICT, certain obstacles need to be overcome, especially inequalities both among and
within countries regarding the access to these technologies and the ability to use them [
23
].
However, it should be stressed that, taking into account the multidisciplinary nature of
ICT for development research, various theoretical paths have been explored to investigate
the adoption, use and impact of ICT in world’s societies. Many various forms of ICT
applications and their contextual use and subsequent socio-economic impacts have been
monitored by researchers from information systems (IS), development economics, social
science and consumer studies over the years However, skepticism regarding the success
of ICT-led initiatives and concerns regarding their actual and potential challenges have
also been discussed in academic literature [
24
–
31
]. It is, therefore, recognized that this
subject is an important aspect of current management practice, while at the same time
research related to the broadly understood development of the economy and societies in
the twenty-first century.
Considering the importance that the European Union gives to a harmonious digital
development among all of its member states, the key issue is to assess how the situation
evolves in particular EU countries. The current research helps to achieve this and sheds
light on the issue of digital convergence. The authors intend to provide a complete and
updated analysis of the development of digital economy and society within the 28 Member
States of the European Union (EU-28) (The study included the United Kingdom, which
is no longer a member of the European Union. However, the DESI published in a given
year is to a large extent calculated on the basis of partial indicators relating to the previous
calendar year. Therefore, due to the fact that the DESI 2020 value includes data from
2019, the analyzes also took into account the United Kingdom, and the average values
were calculated for 28 Member States.). The main objective of the paper is, to present the
development of the digital economy and society in the Countries of the European Union in
the light of the digital convergence of these markets. For analytical purposes, the objective
of empirical research is also defined, which is to identify and exemplify changes in cluster
structures in the digital economy and society index in the period 2015–2020 in the context
of the development of the digital economy and society in the EU-28. The interest of
the authors in this subject is related to the challenges and dilemmas that are emerging
regarding changes in economies and societies in the context of the progressive transition
from an industrial-based economy to an economy based on digital products and services,
relating to data, information and, finally, knowledge, which are decisive for achieving and
maintaining a certain competitive position.
The work formulated research hypotheses, including the main hypothesis and two
specific hypotheses. The main hypothesis is that “in the EU-28 countries, the development
of the digital economy and society is visible in 2015–2020”. On the other hand, the specific
hypotheses are as follows:
HS1: The development of the digital economy and society of the Countries of the
European Union is not homogeneous and specific groups of countries (clusters) are distin-
guished taking into account the similarity of their performance in this area;
HS2: The levels of indicators and the dynamics of their value changes over the period
2015–2020 in the five underlying areas of DESI analysis, i.e., connectivity, human capital,
use of internet services, integration of digital technology, and digital public services which
are diverse, although they are all pro-growth in nature, also for the clusters/groups of
EU-28 countries studied. Their rate of change is not even.
The added value of the research is closing the research gap related to the identification
of similarities (cluster grouping) among EU countries in their levelof development of the
Energies 2021,14, 2717 3 of 26
digital economy and society. This work will also answer the question of whether the
EU-28 countries are on the path towards digital convergence. Filling this gap should help
to capture the overall picture of the results achieved by the countries of the community:
their nature (pro-growth/ pro-fall/unspecified), the dynamics of change, and it may
constitute the basis for the policies formulated, taking into account individual countries
and their groups (clusters), in order to increase the chances of “catching up” among those
countries with similarities with the lowest values of structures in DESI. The concept of
the Digital Single Market means not only creating a framework but ensuring the most
uniform possible conditions (legal, economic, technological, including infrastructure) and
opportunities for the development of the digital economy and society in the context of the
European Union as a whole. Filling this gap can be seen, therefore, as a contribution to
a change in the perception of the “philosophy” of the creation of the European Union’s
policy from the current one, aimed de facto at reducing polarization between countries
(the context of divergence) in order to increase the similarity in the levels of development
of economies and digital society (convergence), leading only to leveling the playing field
between countries, but for the actual long-term digital development in the aspect of the
European Single Market.
Moreover—and this should be particularly emphasized—the analysis of conditions in
this respect is one of the basic elements necessary in the strategic planning process, includ-
ing in the development of policies. This applies to every sector of the economy, including
those that fulfill critical functions, such as the arms industry, banking, telecommunications
or energy [
32
–
34
]. Within the energy sector, for example, the transformation of this market
is indicated, taking into account its digitization. For entering the market of the so-called
smart grids, interconnected and interoperable trading systems, as well as energy manage-
ment, with appropriate use of the potential of artificial intelligence, will undoubtedly result
in a change for both energy suppliers and consumers (also prosumers and their position
on the market). Moreover, progress in the digitization of energy has a direct impact on
the level of security, efficiency, availability and durability of energy systems. Therefore,
it is important to analyze the conditions for the development of the digital economy and
society as a basis for detailed analysis related to e.g., the development of digitization in the
energy sector. It is impossible to separate the context, in this case, of general determinants
from their detailed analysis. This paper is, therefore, a specific introduction to the issue
of analyzing the conditions for the development of the digital economy and society of
EU countries, which may be a starting point for detailed analysis, e.g., concerning the
digitization of the energy sector.
To achieve these goals the paper is organized as follows: Section 2presents a literature
review on the issues of digital economy and society in the context of digital convergence,
in Section 3the authors present a methodological framework for measuring the digital
convergence in EU-28 Member States; Section 4includes the analyses of collected data,
using basic measures of dynamics of time series and the cluster analysis. In Section 5the
authors present the discussion of findings and, in Section 6, the conclusions.
2. The European Union’s Digital Economy and Society and Digital Convergence
Digital economy means the economy of goods and services whose development,
production, sale or supply are wholly dependent on digital technologies [
35
]. It can also
be defined as “a combination of several general-purpose technologies (GPT) and many
economic and social activities carried out by people via the Internet and related technolo-
gies. It includes the physical infrastructure on which digital technologies (broadband lines,
routers) are based, the devices used for access (computers, smartphones), the applications
that power it (e.g., Google, Salesforce), and the features that provide it (Internet of Things,
data analytics, cloud computing)” [
36
]. In this respect, digitalization is seen as the inte-
gration of data and the Internet into production processes, new forms of consumption
within households and the public sector, capital formation, cross-border flows and finance.
Definitions of the digital economy in the literature of the subject are presented in different
Energies 2021,14, 2717 4 of 26
sources [
37
–
39
]. The analysis of the definition of the digital economy clearly indicates its
evolutionary nature in a timely context, but it should nevertheless be emphasized that
all definitions give some acknowledgment that digital technologies of some kind are the
foundation for the digital economy [
40
–
42
]. However only a few, in their explanations,
identify the production of these technologies and related foundational services as part
of—indeed as the core of—the digital economy [
43
]. One of the current comprehensive
definitions is proposed in the following: “The Digital Economy incorporates all economic
activity reliant on, or significantly enhanced by the use of digital inputs, including digital
technologies, digital infrastructure, digital services and data. It refers to all producers and
consumers, including government, that are utilising these digital inputs in their economic
activities” [
44
]. It is worth mentioning here that the global digital economy amounted to an
estimated USD 11.5 trillion (16% of global GDP) in 2015 and is forecast to total over USD
23 trillion (24% of global GDP) in 2025 [
45
]. The digital economy is becoming increasingly
relevant for growth in the world’s countries. The United Nations’ Economic Commis-
sion for Latin America and the Caribbean (ECLAC) estimates that the digital economy
contributed an average 3.2% to GDP in Argentina, Brazil, Chile and Mexico combined.
However, in Japan it is 6.8%, in the United States of America 6.4% and in the European
Union 5% [46].
On the other hand, the concept of ‘digital society’ is closely linked to the digital
economy, as the infrastructure created within the economy is the basis for the creation
of a digital society. There are many definitions of the concept of digital society in the
literature of the subject, which analyze their contemporary usage in different research
approaches and discuss their social, political, cultural, ethical and economic relevance
and impact [
47
,
48
]. In a fairly broad sense, the digital society can be described as “a
modern, progressive society that is formed as a result of the adoption and integration
of Information and Communication Technologies (ICT) at home, work, education and
recreation, and supported by advanced telecommunications and wireless connectivity
systems and solutions” [49].
As indicated earlier, the development of the digital economy and society is cited as
the foundation of growth in the twenty-first century. The information and communication
technology (ICT) sector plays a particular role in this regard, the importance of which for
the development of the world economy, including the economies of its regions (Europe,
America, Asia, etc.), is expressed by dynamic growth. Key trends in the sector include cloud
technologies, Big Data, the Internet of Things, and cybersecurity. It is worth mentioning
that the revenues of segments covered by key trends are growing at a rate of several to tens
of percent per year (and digitally advanced sectors are growing on average 2.8 times faster
than other sectors) and are projected to grow further [
50
], with significant competitive
advantages and savings. As already mentioned, the development of the digital economy
applies to every area, including the energy sector [
51
,
52
]. The current challenges and
opportunities related to the digitization of the energy sector and the development of this
area of the economy include: [53–55]:
-
The transition of electric power supply systems to the Industrial Internet of Things con-
cept (using a network of sensors to monitor energy demand, manage its transmission
and storage);
- Using Artificial Intelligence to optimize energy transmission and distribution;
-
Management of energy resources and logistics using e.g., blockchain, which in turn
increases the potential of operational efficiency in the activities of energy companies;
-
Monitoring of changes in demand with the use of smart grids and models of respond-
ing to changes in this area, which allows not only monitoring but also an autonomous
response to these changes through appropriate energy distribution;
-
Creating the so-called Virtual Power Plants, i.e., systems integrating various energy
sources for the purposes of distribution management and demand production control;
-
Creating energy management systems by consumers and prosumers to optimize
energy production and consumption, along with the analysis and offer of optimal
Energies 2021,14, 2717 5 of 26
energy use from various sources. For the purposes of such a system, the so-called
smart meters or IoT devices at the consumer (mobile reporting applications). The use
of such systems also has an educational value, related to the increase in environmental
awareness of users;
-
The use of digital modeling to design both new energy networks, as well as to improve
the existing ones. The use of this tool will have a direct impact on the operational
efficiency of economic entities from the energy sector, as it will allow for faster data
analysis (compared to existing solutions) on the basis of real energy networks to
prepare new investments, including reducing the costs of designing new installations;
-
The use of blockchain, i.e., security and automation of transactions within the energy
network, e.g., when selling micro-amounts of energy between prosumers;
-
building systems in the form of the so-called “digital twins”. A digital replica of
a physical object works in real-time with this object, making decisions regarding,
for example, optimization of the operation and maintenance of physical fixed as-
sets, production systems and processes in energy, design, organizing information,
estimating project profitability, improving update processes, etc.
Emerging development challenges in the global economy, including information
and communication technologies, have become an important reference point in the EU
policy and EU strategic orientation [
56
,
57
]. The European Union’s Europe 2020 development
strategy [
58
] focused on three priorities to address the challenges of the coming decade [
59
–
61
]:
1. Smart, knowledge-based and innovation-based development;
2.
Sustainable development (efficient use of resources, more environmentally friendly
while increasing competitiveness);
3.
Inclusive development (social, territorial cohesion, with a high level of employment
of the population of EU countries).
These priorities relate to the so-called flagship initiatives, which should include the
Digital Agenda for Europe. It requires the best use of information and communication
technologies to create a sustainable digital future [
19
]. In this respect, the Agenda identified
seven priority areas for action [62]:
•The creation of the Digital Single Market;
•
Improving the framework conditions for interoperability between information and
communication technology products and services;
•Increase trust in the Internet and the security of its operations;
•Ensuring access to a much faster Internet;
•An increase in research and development expenditure;
•Developing digital literal use and inclusion skills;
•
The use of information and communication technologies to address the challenges
facing society, such as climate change, rising medical costs and an aging population.
The creation of the Digital Single Market has been identified as the first priority area
of action, however, in order to be able to carry out this task, action is needed in the other
six areas as they are interlinked. In 2015, the Digital Single Market Strategy was adopted,
based on three pillars of [63]:
1.
Ensuring easier access for consumers and businesses to digital goods and services
across Europe;
2.
Creating an appropriate and level playing field enabling digital networks and innova-
tive services to flourish;
3. Maximizing the growth potential of the digital economy.
An important aspect of the changes and policies relating to the Digital Single Market
was the presentation by the Commission in 2018 of the “AI strategy for Europe”, which
assumes activities in the field of technology, ethics, law and economics and in ethics
guidelines for trustworthy artificial intelligence (AI) in 2019 [
64
]. In its Communication of
June 2020, “A decisive moment for Europe: repair and preparation for the next generation”,
the European Commission made it clear that the recovery of the EU economy after the
Energies 2021,14, 2717 6 of 26
end of the COVID-19 pandemic should take place thanks to the Digital Single Market [
65
].
That is why it is so important that the digital market in the EU is truly a single market,
which promotes its global competitiveness and its ability to achieve competitive advantages
in the future as well.
Research on this issue relates to the subject of broadly understood convergence,
i.e., the theoretical conceptualization of the problem of equalization of the leveling of
development of EU countries and, in this regard, the digital economy and society. The term
convergence is derived from Latin convergere and means the fact that two or more things,
ideas, etc. become similar or come together. The concept of convergence applies both
in science and natural sciences, but also in the humanities and social sciences, including
economics (e.g., a convergence of countries in terms of per capita income) or management
science (strategic convergence of enterprises). In etymological terms, convergence means
the likeness of individuals in a certain respect, which occurs economically and technically
when the variation in the level of development under examination and in relation to the
population studied is reduced or explained as reducing the development distance (catching
up) by countries with initially lower/low development rates compared to countries with
higher/high levels of development [66].
The authors of the paper adopted the author’s definition of the concept of ‘digital
convergence of EU markets’ as a multidimensional phenomenon related to the similariza-
tion of EU economies and societies in terms of access, use and digitalization outcomes,
including access to and use of modern technologies and forms of social communication,
taking into account systemic actions in this field. However, it should be borne in mind that
the aim of the digital convergence processes of EU markets is not to achieve a single fully
homogeneous market but to create a market in which individual countries can implement
their own specific policies in line with the overall dimension of horizontal actions.
From an economic point of reference, at least two-pillar dimensions of the digital
convergence of EU markets should be taken into account:
1.
The horizontal dimension of economy-wide actions, including the regulation of
universal law, solutions and universal capabilities [67];
2.
The dimension of specific actions based on analysis, understanding of barriers and
development opportunities of countries, but also specific sectors, industries, groups
of actors, etc. [68,69].
On the other hand, there are four dimensions (which, however, are differentiated by
education, income, occupation, place of residence, as well as the current stage of life of the
members of the society concerned) [70,71]:
1.
Motivational dimension, related to the mental approach of society to modern tech-
nologies and means of communication;
2. The material dimension relating to society’s access to these tools, resources, etc;
3.
The personal dimension, concerning the ability of members of the public to acquire,
maintain and upgrade acquired capacities;
4. The dimension of the use of technology and the social impact achieved.
The development of the digital economy and society is a multifaceted issue, economic,
social and technological in nature. Most often, certain components are taken into account
in the studies including, in particular:
1.
Willingness to develop on the resource and skills side, which are necessary for
its occurrence;
2.
An increase, expressed in the availability and use of the necessary tools, techniques,
methods, functions, etc. on the road to digital development;
3. Impact–economic impact and effects obtained.
Furthermore, an important element of these processes is also the problem of media
convergence, including their accessibility (infrastructure), use (including aspects of legisla-
tion) and the resulting effects on the economy and society. In this respect, six dominant
interpretations of this term are revealed:
Energies 2021,14, 2717 7 of 26
- The convergence of networks;
- The convergence of terminals;
- The convergence of services;
- The convergence of rhetoric;
- The convergence of markets;
- The convergence of regulatory regimes.
The first one implies that, when digitized, any network can be used to transmit
all kinds of digital signals, provided that speed and bandwidth are high enough] [
72
].
Baldwin et al. [73]
envisioned the development of a “full service network”, a network that
would integrate telephony, data and video, providing a broad range of communication
services and information. It means one network for communication purposes.
Another interpretation of convergence is the convergence of terminals, which can be
analyzed as “the coming together of consumer devices such as the telephone, television
and personal computer” [
74
]. Some authors [
75
,
76
] assume even that all terminals could be
reduced to one [
28
,
41
]. However, observing current trends, it can be seen that different types
of terminals are becoming less obvious, as many of them can be used for multiple purposes,
different terminals are still constructed for and used in different social settings and the
number of specialized terminals is still increasing [
72
]. The third type of interpretation of
media convergence in the literature of the subject is the convergence of services. As Beyer
et al. rightly point out [
77
] new services have been developed within and across media
platforms. Nevertheless, although the services cross-media platforms, we can observe that
the platforms themselves are still relevant.
The term “rhetorical convergence” is used to describe the process where new genres
are created by mixing traits known from genres in different earlier media [
72
]. In Nielsen’s
words there should be “a rhetorical convergence in which expressions and genres would
no longer be distinct, but grow into one unified language” [72].
The aforementioned interpretations of media convergence, i.e., a convergence of net-
works, terminals and services, was further expected to lead to a convergence of markets.
As it is noticed by Picard [
78
], or Hoskins et al. [
79
], ICT, telecom and media companies
would merge or form alliances, so we would see the development of multimedia compa-
nies. However, the market itself is not and most likely will never be fully homogeneous.
Fagerjord and Storsul rightly emphasize that in the market web, some corporations seek
control of the whole value chain through vertical expansion, whereas others specialize in
narrow sub-markets [72].
The perceptions of convergence (presented above) with communication networks,
terminals, services and markets have had a strong impact on the political discourse. As in-
dicated by the Østergaard [
80
] regulations of telecommunications, media and other media
services had earlier been closely attached to the networks delivering the service. Therefore,
consultations are being undertaken within the EU on the impact of planned legislative
changes on convergence processes. This is justified by both horizontal and specific policy
considerations (it is worth mentioning that, as early as the late 1990s, some researchers
pointed to the inconsistencies between the technological and the regulatory levels [
81
–
83
]).
Regulations in the EU and in the individual countries have been integrated into common
regulatory frameworks for electronic communications, so the networks have one common
regulatory framework.
Measuring the convergence process itself, both in relation to the above-referred re-
search perspectives and to the Union market as a single market composed of many, is not
an easy task, but nevertheless, the development of appropriate measurement tools is nec-
essary in this respect [
84
]. They form the basis for carrying out analyses, evaluations and
drafting of amendments. However, traditional methods and tools for measuring economic
development are often unable to capture the changes taking place in the digital world.
Therefore, different methods are developed and different indicators are used to assess and
compare the digital development performance of different economies and societies [
85
–
88
].
Energies 2021,14, 2717 8 of 26
For the purpose of measuring progress in the development of the digital economy
and society, a Digital Economy and Society Index (DESI) has been developed and adopted
in the European Union, taking into account five baseline sub-indicators, i.e., connectivity,
human capital, use of the internet, integration of Digital Technologies, and Digital public
services. At the level of its respective dimensions, DESI refers to the five main EU policies
relevant to the development of the digital economy and society. These areas are interlinked,
so the development of the digital economy and society requires the implementation of
improvement processes in all these areas at the same time. Connectivity covers the deploy-
ment of broadband infrastructure and its quality; human capital relates to skills needed to
take advantage of the possibilities offered by a digital society. These are the most relevant
dimensions of DESI because they represent the infrastructure of the digital economy and
society. Integration of digital technology captures the digitalization of businesses and de-
velopment of the online sales channels. It mirrors, therefore, the use of ICT by the business
sector, which is one of the most important drivers of growth. The last two dimensions are
the use of the Internet, reflecting the variety of activities performed by citizens already
online, and digital public services, related to the digitalization of public services, focusing
on eGovernment. They are enabled by the infrastructure and their contribution to the
digital economy and society is strengthened by the quality of such infrastructure [89].
The DESI indicator and its sub-indicators not only provide an opportunity to assess
the current state of play but also to compare the achieved position of the economy in
relation to others in the EU. Studies in this regard, included in the form of time series,
also allow us to determine the dynamics and directions of change, and on the basis of
them to formulate assessments of these changes in the development of the economy
and digital society of individual EU countries, their groups/clusters or the European
Union as a whole [
90
–
95
]. Finally, research efforts illustrate whether (or not) progress
is being made towards a Digital Single Market as a consequence of the existing digital
convergence (divergence) between EU countries in the light of economic development
policies and the digital society [
96
]. There is also the International Digital Economy
and Society Index (I-DESI) which enables trend analysis and comparison of the digital
performance of 45 countries: the EU27 Member States and 18 non-EU countries around the
world (Australia, Brazil, Canada, Chile, China, Iceland, Israel, Japan, Mexico, New Zealand,
Norway, Russia, Serbia, South Korea, Switzerland, Turkey, the United Kingdom, and the
United States). Comparisons enable the identification of gaps between the performance
and capabilities of the 45 countries studied and are useful to identify areas requiring
investments and actions to reach the levels of the best performing global countries and to
monitor the progress of the 2020 EU digital strategy.
It should be remembered that the adopted digital strategy of the European Union has
a direct impact on the functioning and development of national economies, their enterprise
sector, and in particular those that use the achievements of the digital economy in their
activities, including those relating to the broadly understood industry 4.0. As it is depicted
in the literature review, digital economy and society are very broad terms related to the
effects of industrial revolutions, which are conditions for the development of business, i.e.,
economic entities and societies. These are issues that require a holistic view, also in terms
of long-term effects.
It cannot be forgotten yet that in respect to technological convergence of the EU
countries that has been proved econometrically for the years 1995–2009 (Research based on
several dozen econometric models [
97
]), convergence alone is not enough if the mechanism
is mainly imitation rather than innovation. The implementation of progress must be
based on a qualitative reference in order to create changes. This means that the digital
convergence of EU markets should move towards achieving the best possible position
for a country, with continuous improvements, including those with the best results, for
pro-innovation activities.
Energies 2021,14, 2717 9 of 26
3. Materials and Methods
Data on the evolution of the Digital Economy and Society Index (DESI) and its five sub-
benchmarks have been used to measure the progress made by European Union countries
in the development of the digital economy and society and to assess whether and in what
areas the digital convergence process takes place. The analysis was based on data from
2015 to 2020. They covered 28 countries of the European Union. The basic data source
was the data available on the European Union website in the Euro-stat database [
98
].
The DESI has a three-level structure: dimensions, subdimensions, indicators. In our
analysis, we focused on the DESI and its five highest level dimensions that make up the
overall DESI index. As some dimensions, subdimensions, and indicators are more relevant
than others, they were given higher weight in the computation of the final index for each
country. Weights attributed to the DESI dimensions are as follows: Connectivity 25%;
Human capital 25%; Use of internet services 15%; Integration of digital technology 20%;
Digital public services 15%. Figure 1presents data concerning the DESI indicator and its
components using adequate country indicators whereas Appendix A(Tables A1 and A2)
contains initial data regarding DESI and its particulars (before weights).
Energies 2021, 14, 2717 9 of 27
to the effects of industrial revolutions, which are conditions for the development of busi-
ness, i.e., economic entities and societies. These are issues that require a holistic view, also
in terms of long-term effects.
It cannot be forgotten yet that in respect to technological convergence of the EU coun-
tries that has been proved econometrically for the years 1995–2009 (Research based on
several dozen econometric models [97]), convergence alone is not enough if the mecha-
nism is mainly imitation rather than innovation. The implementation of progress must be
based on a qualitative reference in order to create changes. This means that the digital
convergence of EU markets should move towards achieving the best possible position for
a country, with continuous improvements, including those with the best results, for pro-
innovation activities.
3. Materials and Methods
Data on the evolution of the Digital Economy and Society Index (DESI) and its five
sub-benchmarks have been used to measure the progress made by European Union coun-
tries in the development of the digital economy and society and to assess whether and in
what areas the digital convergence process takes place. The analysis was based on data
from 2015 to 2020. They covered 28 countries of the European Union. The basic data source
was the data available on the European Union website in the Euro-stat database [98]. The
DESI has a three-level structure: dimensions, subdimensions, indicators. In our analysis,
we focused on the DESI and its five highest level dimensions that make up the overall
DESI index. As some dimensions, subdimensions, and indicators are more relevant than
others, they were given higher weight in the computation of the final index for each coun-
try. Weights attributed to the DESI dimensions are as follows: Connectivity 25%; Human
capital 25%; Use of internet services 15%; Integration of digital technology 20%; Digital
public services 15%. Figure 1 presents data concerning the DESI indicator and its compo-
nents using adequate country indicators whereas Appendix A (Tables A1 and A2) con-
tains initial data regarding DESI and its particulars (before weights).
Figure 1. Value of DESI and its subcomponents for EU-28 countries in 2015 and 2020 Source: Our own study based on
European Commission data available on the https://digital-agenda-data.eu/ (accessed on 23 October 2020).
Figure 1.
Value of DESI and its subcomponents for EU-28 countries in 2015 and 2020 Source: Our own study based on
European Commission data available on the https://digital-agenda-data.eu/ (accessed on 23 October 2020).
The empirical research was carried out in two phases. Firstly, basic measures of time
series dynamics, i.e., average absolute increment and average change index, were used to
determine the dynamics of changes in index values. On the other hand, in order to classify
countries in terms of the similarity of the value and structure of the DESI index, a cluster
analysis was used as one of the basic methods of so-called unsupervised grouping [
99
].
Cluster analysis is a set of methods of multidimensional statistical analysis, used to extract
homogeneous subsets of objects of the study population. Finding groups (clusters) of
objects is based on variables that characterize the analyzed objects. The method shown
uses measures of discrepancy or distance between objects when forming clusters. The most
direct way to calculate the distance between objects in a multidimensional space is to
calculate the Euclidean distance. However, often in this analysis a different metric can be
Energies 2021,14, 2717 10 of 26
taken as a measure of distance. In addition, it is also possible to estimate the distance of
objects without using a metric. This research uses the Ward method. This method uses a
variance analysis approach to estimate the distance between the focus. Thus, this method
aims to minimize the sum of squared deviations of any two clusters that can be formed
at any stage of the algorithm [
100
]. The chosen method is considered to be the most
effective, although it aims to create clusters of a small size [
101
]. For the data considered,
no significant linear relationships between the different variables were found. The selection
of the number of clusters is based on the difference between the maximum similarity within
the groups and the minimum [
102
]. On this basis, an optimal number of concentrations
of five was established. Cluster analysis was carried out with the use of the Statgraphics
18 program, while the figures were prepared with the use of Excel and Corel Draw.
4. The Digital Economy and Society in the Countries of the European Union in
2015 and 2020. Convergence or Digital Diversity? Results of Empirical Studies
The DESI synthetic index illustrates the level of development of the digital economy
and society, which today is one of the most important determinants affecting the standard
of living of the population, as well as the competitiveness of the economies of individual
EU countries and the European Union as a single market in the context of the Digital
Single Market strategy. Using the Ward method, based on the DESI data in 2015 and 2020,
the EU-28 countries were divided into cluster structures. Among the EU-28 countries,
5 clusters were distinguished in each of the analyzed years. Dendrograms are presented in
Figures 2and 3.
Energies 2021, 14, 2717 10 of 27
The empirical research was carried out in two phases. Firstly, basic measures of time
series dynamics, i.e., average absolute increment and average change index, were used to
determine the dynamics of changes in index values. On the other hand, in order to classify
countries in terms of the similarity of the value and structure of the DESI index, a cluster
analysis was used as one of the basic methods of so-called unsupervised grouping [99].
Cluster analysis is a set of methods of multidimensional statistical analysis, used to extract
homogeneous subsets of objects of the study population. Finding groups (clusters) of ob-
jects is based on variables that characterize the analyzed objects. The method shown uses
measures of discrepancy or distance between objects when forming clusters. The most
direct way to calculate the distance between objects in a multidimensional space is to cal-
culate the Euclidean distance. However, often in this analysis a different metric can be
taken as a measure of distance. In addition, it is also possible to estimate the distance of
objects without using a metric. This research uses the Ward method. This method uses a
variance analysis approach to estimate the distance between the focus. Thus, this method
aims to minimize the sum of squared deviations of any two clusters that can be formed at
any stage of the algorithm [100]. The chosen method is considered to be the most effective,
although it aims to create clusters of a small size [101]. For the data considered, no signif-
icant linear relationships between the different variables were found. The selection of the
number of clusters is based on the difference between the maximum similarity within the
groups and the minimum [102]. On this basis, an optimal number of concentrations of five
was established. Cluster analysis was carried out with the use of the Statgraphics 18 pro-
gram, while the figures were prepared with the use of Excel and Corel Draw.
4. The Digital Economy and Society in the Countries of the European Union in 2015
and 2020. Convergence or Digital Diversity? Results of Empirical Studies
The DESI synthetic index illustrates the level of development of the digital economy
and society, which today is one of the most important determinants affecting the standard
of living of the population, as well as the competitiveness of the economies of individual
EU countries and the European Union as a single market in the context of the Digital Sin-
gle Market strategy. Using the Ward method, based on the DESI data in 2015 and 2020,
the EU-28 countries were divided into cluster structures. Among the EU-28 countries, 5
clusters were distinguished in each of the analyzed years. Dendrograms are presented in
Figures 2 and 3.
Figure 2. Ward’s dendrogram for the EU-28 Member States in 2015. Source: Our own study.
Figure 2. Ward’s dendrogram for the EU-28 Member States in 2015. Source: Our own study.
The selection of countries for individual clusters was dictated both by the DESI values
obtained and by the values within its internal structures, which consisted of the following
components: connectivity, human capital, use of internet services, integration of digital
technology, digital public services. It can be seen that Cluster 1 includes those countries
whose DESI in 2015 and 2020 was the lowest among the EU-28 countries analyzed. Cluster
5, on the other hand, includes the countries which achieved the highest values in the years
analyzed, both for DESI and for the components separated within it. Detailed data on the
average values of indicators for particular clusters are presented in Figure 4.
Energies 2021,14, 2717 11 of 26
Energies 2021, 14, 2717 11 of 27
Figure 3. Ward’s dendrogram for the EU-28 Member States in 2020. Source: Our own study.
The selection of countries for individual clusters was dictated both by the DESI val-
ues obtained and by the values within its internal structures, which consisted of the fol-
lowing components: connectivity, human capital, use of internet services, integration of
digital technology, digital public services. It can be seen that Cluster 1 includes those coun-
tries whose DESI in 2015 and 2020 was the lowest among the EU-28 countries analyzed.
Cluster 5, on the other hand, includes the countries which achieved the highest values in
the years analyzed, both for DESI and for the components separated within it. Detailed
data on the average values of indicators for particular clusters are presented in Figure 4.
Figure 4. Value of DESI and its subcomponents in a cross-section of isolated clusters. Source: Our
own study.
Figure 3. Ward’s dendrogram for the EU-28 Member States in 2020. Source: Our own study.
Energies 2021, 14, 2717 11 of 27
Figure 3. Ward’s dendrogram for the EU-28 Member States in 2020. Source: Our own study.
The selection of countries for individual clusters was dictated both by the DESI val-
ues obtained and by the values within its internal structures, which consisted of the fol-
lowing components: connectivity, human capital, use of internet services, integration of
digital technology, digital public services. It can be seen that Cluster 1 includes those coun-
tries whose DESI in 2015 and 2020 was the lowest among the EU-28 countries analyzed.
Cluster 5, on the other hand, includes the countries which achieved the highest values in
the years analyzed, both for DESI and for the components separated within it. Detailed
data on the average values of indicators for particular clusters are presented in Figure 4.
Figure 4. Value of DESI and its subcomponents in a cross-section of isolated clusters. Source: Our
own study.
Figure 4.
Value of DESI and its subcomponents in a cross-section of isolated clusters. Source: Our
own study.
Looking at the data by country, the DESI indicator showed changes compared to the
two surveyed years, i.e., 2015 and 2020. The noted change should be assessed positively,
as it entails a higher level of digitization of the economy and society during the period
considered in the EU, which in turn entails the gradual acquisition of the assumptions made
in the Digital Single Market strategy. In the years adopted, the analysis of 14 out of 28 EU
countries showed DESI values below average, and the other half above the EU average.
Countries with below-average EU values in 2015 and 2020 included Bulgaria, Cyprus,
the Czech Republic, Greece, France, Croatia, Hungary, Italy, Latvia, Poland, Portugal,
Energies 2021,14, 2717 12 of 26
Romania, Slovenia, Slovakia. The lowest DESI was recorded in 2015 in Greece (26.13 points)
and in Bulgaria in 2020 (36.44 points). In contrast, the following EU countries achieved
DESI above the EU average: Austria, Belgium, Germany, Denmark, Estonia, Spain, Finland,
Ireland, Lithuania, Luxembourg, Malta, the Netherlands, Sweden and the United Kingdom,
with the highest values recorded for Denmark, Finland and Sweden in both years.
When analyzing data on the performance of individual countries in relation to the
EU-28 average (Table 1), it can be concluded that:
•
In 2015, the lowest rate recorded for Greece was 67.16% of the EU-28 average and the
highest for Denmark was 146.44%;
•
In 2020, the lowest rate recorded for Bulgaria was 69.25% of the EU-28 average and
the highest for Finland was 137.42%.
Table 1.
Data summary and evaluation of the obtained values in terms of DESI measures and its sub-indicators for the
countries with the lowest and the highest values in relation to the EU average in 2015 and 2020. Source: Our own study.
Share in Relation to the EU-28 Average of
the Lowest and the Highest Score of the
Countries
That Year
Included:
Difference
[P.P.]
Degree of
Recorded
Differences in DESI
Components in the
Analyzed Periods
The Assessment of
Changes
in the Context of
Implementing
Convergence Processes
in a Given Area
Lowest Values
[%]
Highest Values
[%]
Connectivity 2015 56.0 146.2 90.2 degree 3 +
2020 66.7 131.5 64.8
Human Capital 2015 62.4 164.1 101.7 degree 1 +
2020 65.8 159.1 93.3
Use of Internet
Services
2015 48.2 145.1 96.9 degree 4 +
2020 61.9 131.6 69.7
Integration of
Digital Technology
2015 52.3 164.1 111.8 degree 2 -
2020 43.2 179.6 136.4
Digital public
services
2015 41.0 153.9 112.9 degree 5 +
2020 67.3 124.1 56.8
DESI 2015 67.2 146.4 79.2
−+
2020 69.3 137.4 68.1
Clarification: degrees 1–5 mean ordering values of indicated differences between 2020 and 2015, where degree 1 means the lowest
and degree 5 means the largest difference; “+” positive rate of indicated changes in the context of implementing indicated changes in
convergence processes (enlarging convergence); “
−
” negative rate of indicated changes in the context of implementing indicated changes
in convergence processes (reducing convergence).
The decreasing gap between the lowest and highest rates in relation to the EU average
(79.28 pps in 2015 and 68.17 pps in 2020) shows that convergence between EU-28 countries
is gradually increasing which, overall, is a positive development. The positive results are
also confirmed by the improvement in the relationship between the results of the countries
with the highest and lowest rates, which decreased from 2.18 in 2015 to 1.98 in 2020.
Table 2shows the dynamics of the average DESI values and their individual sub-factors
for the period 2015–2020 in the cross-section of the isolated clusters. The results of studies on
the development of DESI in individual clusters have led to the following conclusions:
1.
For Cluster 1 in 2015, the DESI was 29.08 points and in 2020 it was 43.54 points,
an increase of 49.74%. The achieved indicator values for Cluster 1 were below the
UNION average;
2.
For Cluster 2 in 2015, DESI was 32.40 points and in 2020 45.13 points, an increase of
39.32%. The DESI values obtained for this cluster were below the Union average;
3.
For Cluster 3 in 2015, DESI was 41.67 points, then increased to 53.31 in 2020, i.e.,
by 27.92%, and its values in both 2015 and 2020 were above the Union average;
4.
For Cluster 4 in 2015, DESI accounted for 42.08 points, after which it increased to
59.81 in 2020, i.e., by 42.13%, and its values in both 2015 and 2020 were above the
Union average;
Energies 2021,14, 2717 13 of 26
5.
For Cluster 5 in 2015, DESI was 55.00 points, then increased to 69.72 in 2020, i.e.,
by 26.76%, and its values were both above the EU average in 2015 and 2020.
Table 2. Dynamics of average DESI and individual sub-rates in 2015–2020 (2015 = 100%).
Cluster Number DESI Connectivity Human Capital Use of
Internet Services
Integration of
Digital Technology
Digital Public
Services
1 149.74 156.41 133.23 162.23 176.41 137.68
2 139.32 205.16 93.36 115.11 103.81 226.47
3 127.92 170.98 105.84 126.00 115.10 129.04
4 142.13 142.73 121.88 126.33 200.96 148.89
5 126.76 146.94 110.94 119.61 145.67 120.10
Average EU-28 135.23 160.81 112.04 128.92 141.57 143.39
Source: Our own study.
All clusters showed an increase in the mean DESI. Cluster 1 (almost 50%), Cluster 4
(just over 42%), Cluster 2 (just over 39%), and Cluster 3 and Cluster 5 (almost 28% and 27%
respectively) showed the greatest momentum between 2020 and 2015.
Sub-indicators representing the individual components of the DESI indicator were
also analyzed. In the area of the first partial indicator on
Connectivity
in 2020, a significant
change was observed compared to 2015, as it increased by 60.8%. It should be noted that
the average value of this indicator was significantly higher than that of the overall DESI.
The highest growth rates were recorded in countries such as Cyprus, Italy and Hungary,
while the smallest was recorded in Estonia, Latvia and Slovenia.
In both 2015 and 2020, 12 countries recorded below the EU-28 average, with countries
such as Austria, Bulgaria, Cyprus, Greece, France, Croatia, Ireland, Italy and Slovakia in
both years. Hungary, Poland and Portugal also scored below the EU average in 2015, while
the Czech Republic, Lithuania and the United Kingdom recorded below the EU average in
2020. In 2015, Cyprus (17.44 points), Greece (18.93 points) and Croatia (22.60 points) scored
the lowest score in the connectivity area, while in 2020 it was again Greece (33.37 points)
and Cyprus (38.46 points), as well as Bulgaria (38.50 points). It should also be noted that in
the countries with the lowest connectivity rates among the EU-28 in 2015, their ratio to the
EU average improved in 2020, with the exception of Bulgaria, which deteriorated from the
average. An unfavorable change in this relationship also occurred in the Czech Republic,
which in 2020 was among the top five countries with the lowest rate in this area.
The above-average EU-28 score, both in 2015 and 2020, was recorded by 16 countries,
both of which concerned countries such as Belgium, Germany, Denmark, Estonia, Spain,
Finland, Luxembourg, Latvia, Malta, the Netherlands, Romania, Sweden and Slovenia.
As already mentioned, the Czech Republic, Lithuania and the United Kingdom have
deteriorated compared to the EU-28 average, while it has improved in Hungary, Poland
and Portugal. Latvia (45.52 points), Denmark (45.11 points) and Luxembourg (45.10 points)
scored the highest in the area of connectivity in 2015. In 2020, these were again Denmark
(65.82 points) and Luxembourg (63.35 points) as well as Sweden (64.37 points). Analyzing
the performance of the top five countries in 2015, it was found that in all these countries in
2020 their ratio to the EU-28 average deteriorated, which means that the growth rate of
their indicators in the area of connectivity was lower than the average in the Union. In 2020,
Spain was in the top five, which improved in relation to the EU-28 average. It is worth
noting that the list of the top 5 best or worst performing countries among EU countries did
not change during the period considered with the exception of the Netherlands, which left
the top five best-ranked countries in this area, Italy, which left the top five, and the Czech
Republic, which were among the worst five.
By generalization of the presented results in relation to the EU-28 average (Table 1),
it can be concluded that:
Energies 2021,14, 2717 14 of 26
•
In 2015, the country with the lowest rate in Cyprus accounted for 56.0% of the EU-
28 average and Latvia’s highest-rate country accounted for 146.2%;
•
Greece had the lowest score in 2020 (66.7% of the EU-28 average), while Denmark had
the best result (131.5% of the EU-28 average).
It can be concluded that, relatively, there is a noticeable decreasing polarization in
the area of connectivity (the difference between the lowest and highest rates in relation
to the EU-28 average decreased from 90.2 pps in 2015 to 64.8 percentage points in 2020).
This means that the countries’ performance is getting closer to the EU-28 average, which is
conducive to their convergence. This is also confirmed by the change in the relationship be-
tween the performance of the countries with the highest and lowest rates, which decreased
from 2.6 in 2015 to less than 2 times in 2020. However, it is still quite high.
Looking at the 2015 and 2020 data for each cluster, it was found that:
•
In 2015, the average indicator for Cluster 1 and Cluster 2 was below the EU-28 average,
Cluster 3 was very close to Cluster 3, while Clusters 4 and 5 were above the EU-28 average;
•
In 2015, almost all countries in Cluster 1 and Cluster 2 (with the exception of Romania
in Cluster 1 and the Czech Republic in Cluster 2) had a connectivity ratio below the
EU-28 average. In the third cluster, they were three out of four countries (Austria,
Ireland, Portugal) and in the Cluster 4 there was one country (France);
•
In 2020, the average connectivity value for the first cluster was below the EU-28 aver-
age, while in the remaining clusters it was higher than the EU-28 average;
•
In 2020, in Cluster 1, all countries did not reach the EU average, Cluster 2 below
average had one country (Italy), in Cluster 3–three countries (Austria, France and
Lithuania) and in Cluster 4 there were another two (Ireland and the United Kingdom).
In all clusters, the average connectivity indicator increased in the period 2015–2020,
with Cluster 2 (an increase of just over 105%), followed by Cluster 3 (an increase of nearly
71.0%) Cluster 1 (just over 56.0%) and the smallest for Cluster 5 (close to 47.0%) Cluster 4
(close to 43.0%).
In the second area of the DESI–
Human Capital
, the sub-indices of each country also
increased. In 2015, the average score at EU-28 level was only 12.0% higher. In all countries
except Latvia, there has been an improvement in internet literacy and basic and advanced
digital skills held by Citizens of the European Union in the years analyzed, as measured
by indicators that make up the human capital index. The highest percentage increase was
recorded in countries such as Lithuania, Romania and Croatia, while Denmark, Greece,
Italy and Latvia were the least dynamic, with the index deteriorating as mentioned.
In 2015, 14 countries scored below the EU average, while in 2020 as many as 16 coun-
tries scored below. In 2015 these were Bulgaria, Cyprus, Greece, Spain, Croatia, Hungary,
Italy, Lithuania, Latvia, Poland, Portugal, Romania, Slovenia and Slovakia, and in 2020 they
were joined by the Czech Republic and France. In both 2015 and 2020, the lowest human
capital ratios were in Romania (27.47 points and 33.16 points), Bulgaria (29.18 points and
33.92 points) and Italy (30.81 points and 32.46 points). The above-average EU-28 score in
both 2015 and 2020 was recorded in 12 countries: Austria, Belgium, Germany, Denmark,
Estonia, Finland, Ireland, Luxembourg, Malta, the Netherlands, Sweden and the United
Kingdom, and in 2015 the Czech Republic and France. In 2015, Finland (72.21 points),
Sweden (61.26 points) and Denmark (60.63 points) achieved the highest human capital
score. In 2020, these were again Finland (78.44 points) and Sweden (71.72 points) as well as
Estonia (66.66 points). It is worth noting that the list of the top 5 countries has not changed
significantly with the exception of Denmark, which was replaced by the Netherlands.
In turn, Poland and Portugal left the five most vulnerable countries in this area, replaced
by Greece and Latvia.
In relation to the EU-28 average (Table 1), it was concluded that:
•
In 2015, the country with the lowest rate in this area, Romania, accounted for 62.4% of
the EU-28 average, while Finland’s highest-rate country accounted for 164.1%;
Energies 2021,14, 2717 15 of 26
•
Italy had the lowest score in 2020 (65.8% of the EU-28 average), while Finland had the
best result (159.1% of the EU-28 average), of which it can be concluded that only a
slight reduction in polarization in the area of human capital is noticeable in relative
(percentage) terms, as the range between the lowest and highest rates in relation to
the EU-28 average decreased from 101.7 percentage points in 2015 to 93.3 percentage
points in 2020. The ratio of the performance of the countries in the extreme places in
the ranking of this indicator has also changed only slightly. In 2015, the ratio of the
best performance in the area of human capital of Finland to the lowest rate in Romania
was 2.6, while in 2020 the ratio for the highest in the area of human capital of Finland
and Italy was 2.4.
The analyses carried out in terms of isolated clusters also made it possible to draw the
following conclusions:
•
In 2015, the average indicator for the Human Capital component of Cluster 1 and
2 countries was below the EU average, Cluster 3 was almost at the EU average, while
Clusters 4 and 5 were above the EU average;
•
Almost all the countries included in Clusters 1 and 2 (with the exception of the Czech
Republic in Cluster 2) had a sub-indicator value in this area below the EU average.
In the third Cluster, this included three of the seven countries (Spain, Lithuania,
Portugal) and in Cluster four there were two countries (Latvia and Slovenia);
•
In 2020, the average value of Human Capital in Clusters 1, 2 and 3 was below the EU
average, with lower values than the EU average for all countries in these Clusters
except Austria in Cluster 3;
•
In 2020, in Clusters 4 and 5, the average value of the indicator in the cluster, as well
as the values of indicators for all countries in these clusters, were higher than the
EU-28 average.
Noteworthy is the fact that in the period 2015–2020 only four clusters increased the
average human capital ratio. These were Cluster 1 (an increase of just over 33% in the
cluster), Cluster 4 (an increase of almost 22%), Cluster 5 (an increase of almost 11%) and
Cluster 3 (an increase of less than 6%). On the other hand, the average human capital ratio
for Cluster 2 countries decreased by just over 6%.
Under
Use of Internet services
, the average sub-index increased by 28.9% between
2015 and 2020, with an average increase of 35.2% overall, significantly less. The highest
percentage increase was recorded in countries such as Romania, Ireland and Poland, while
Luxembourg, Denmark and Estonia were the least dynamic. Of the 28 EU countries,
15 countries scored below the EU average in 2015. These were the following countries in
order, starting with the lowest rate: Romania (21.71 points), Bulgaria, Italy, Greece, Poland
(33.42 points), Portugal, Cyprus, Croatia, Ireland, Austria, Czech Republic, Slovakia,
Slovenia, France, Spain (44.77 points). Among these countries, the level of the indicator
varied significantly as Spain’s index was 2.06 times higher than Romania’s. This group
included Poland, with a relatively poor result, as well as France, a more socio-economically
developed country, yet with a rather high agricultural position.
Among the 13 countries with an indicator above the EU average, Latvia (45.04 points),
Hungary, Belgium, Malta, Lithuania, Germany, Estonia, Luxembourg, the United Kingdom,
the Netherlands, Finland, Sweden, Denmark (65.31 points) were the order. The differences
between countries were much smaller than for countries with indicators below the EU
average, as the spread between Denmark and Latvia was 45% points out and this group in-
cluded 4 eastern European countries (the Baltic States and Hungary). In 2020, the following
16 countries scored below the EU average: Romania (35.89 points), Bulgaria, Italy, Greece,
Portugal, Poland (49.64 points), Slovenia, France, Slovakia, Latvia, Austria, Czech Republic,
Cyprus, Croatia, Hungary, Lithuania (57.32 points). It appears that Romania, Bulgaria, Italy,
Greece, Portugal and Poland remained the weakest countries in this respect. Lithuania and
Latvia, which were previously listed above, also joined this group. The spread between
Romania and Lithuania was 59.7%, significantly less than the five years earlier in this group.
Energies 2021,14, 2717 16 of 26
Among the countries with indicators above the EU average, only 12 countries were
included in 2020: Luxembourg (58.85 points), Spain, Belgium, Germany, Ireland, Estonia, Malta,
the United Kingdom, Denmark, the Netherlands, Sweden, Finland (76.34 points). Lithuania,
Latvia and Hungary, therefore, fell out from this group and Spain and Ireland were promoted.
The spread between Luxembourg and Finland was 29.7%, significantly less than in 2015.
In addition, analyzing data in Table 1, it can be stated that in 2015, the country with
the lowest rate in this area, Romania, accounted for 48.2% of the EU-28 average, while
the country with the highest rate was Denmark at 145.1%. Romania (61.9% of the EU-
28 average) also recorded the lowest score in 2020, while the best one was in Finland
(131.6% of the EU-28 average). Relatively, therefore, there is a clear decreasing polarization
in the area of internet service use, and the gap between the lowest and highest rates in
relation to the EU-28 average has decreased from 96.87 pps in 2015 to 69.71 pps in 2020).
It can, therefore, be concluded that the diversity in the use of online services is decreasing
between the EU Member States. This is also confirmed by the spread between the countries
with the highest and lowest rates in 2015 and 2020, as the ratio of these results has decreased
from 3 times to 2 times. However, it is still substantially high.
The analyses carried out within this component in terms of isolated clusters have
concluded that:
•
In 2015, the average rate for the Use of Internet component of Cluster 1, 2 and 3 coun-
tries was below the EU average, while Clusters 4 and 5 were above that average;
•
Almost all the countries included in Cluster 1 and Cluster 2 (with the exception of
Hungary in Cluster 2 and Belgium, Lithuania and Malta in Cluster 3) had a sub-
indicator value in this area below the EU average. In Cluster 4, they were two of the
seven countries (France, Slovenia);
•
In 2020, the average use of Internet value in Clusters 1, 2 and 3 was below the EU
average, with lower values than the EU average for all countries in these clusters
except Spain in Cluster 3;
•
In 2020, in Clusters 4 and 5, the average value of the indicator in the cluster, as well
as the values of indicators for all countries in these clusters, were higher than the
EU-28 average.
In all clusters, the average Internet usage rate increased in the period 2015–2020,
with Cluster 1 (an increase of just over 62%), followed by Cluster 4 and 3 (an increase of
about 26%) Cluster 5 (by almost 20%) and the smallest for Cluster 2 (just over 15.0%).
In the area of
Integration of Digital Technologies
, the average sub-index increased
by 41.6%, which is more than the Internet service usage rate and the total DESI index.
The highest percentage increase was recorded in countries such as Latvia, Estonia and
France, while Croatia, Greece and Slovakia were the least dynamic. Of the EU Member
States, 15 countries scored below the EU average in 2015. These were the following
countries according to the increasing size of the indicator: Bulgaria (15.28 points), Latvia,
Romania, Poland (17.27 points), Hungary, Italy, Greece, Estonia, Cyprus, France, Slovenia,
Luxembourg, Slovakia, Austria, Germany (28.48 points). The spread between Bulgaria
and Germany was 86.4%, which was significantly lower than for countries below the
EU-28 average for internet service use (2 times difference). Poland was also in the weak
fourth place in this group (in fifth place in Internet services). It is surprising that Germany,
which has such a high level of economic development, is included in this group of countries
(although it is in the best position).
Among the 13 countries with indicators above the EU average, the order was Spain
(29.27 points), Portugal, Lithuania, the United Kingdom, Malta, Croatia, the Czech Repub-
lic, Finland, Belgium, the Netherlands, Sweden, Ireland, Denmark (47.96 points). The range
between the indicators for Denmark and Spain was 63.9%. It is worth highlighting the rela-
tively high positions of Lithuania and the Czech Republic, countries neighboring Poland.
By contrast, in 2020, the following 16 countries scored below the EU average: Bulgaria
(17.86 points), Romania, Hungary, Poland (26.24 points), Greece, Latvia, Italy, Slovakia, Cyprus,
Luxembourg, Germany, Austria, Portugal, Slovenia, Estonia, Spain (41.22 points). The spread
Energies 2021,14, 2717 17 of 26
between Spain and Bulgaria had more than doubled (230.8%). This group mainly included
the same countries as in 2015, but in a slightly different order. A notable exception is France
leaving the group and the inclusion of Spain, which in 2015 was above the EU-28 average.
Among the 12 countries with indicators above the EU average, Croatia (41.47 points),
France, Lithuania, the Czech Republic, the United Kingdom, Malta, Sweden, Denmark,
the Netherlands, Belgium, Finland and Ireland (74.32 points) were among the 12 countries
with indicators above the EU average. The spread between Ireland and Croatia was 79.2%
and between Ireland and the United Kingdom (in the middle of the group) only 37.1%.
It is worth highlighting the relatively high places of Lithuania and the Czech Republic and,
above all, Ireland. Unlike the area of online services, however, the differences between
EU countries have widened in terms of the use of digital technologies. As the data in
Table 1indicate, in 2015, the country with the lowest rate in this area, Bulgaria, accounted
for 52.3% of the EU-28 average and Denmark’s top 164.1%. Bulgaria (43.2% of the EU-
28 average) also recorded the lowest score in 2020, with the best in Ireland (179.6% of
the EU-28 average). Relatively, therefore, there is a clear increase in polarization in the
area of digital use, and the gap between the lowest and highest rates in relation to the
EU-28 average has increased from 111.8 pps in 2015 to 136.4 pps in 2020). In addition,
in 2015 there was more than 3 times the difference between Bulgaria, the country with the
lowest rate, and Denmark leading in this ranking, while in 2020 the difference between
Bulgaria and Ireland was more than 4 times.
The analyses carried out within the Integration of Digital Technology component in
terms of isolated clusters concluded that:
•
In 2015, the average value of this indicator for Cluster countries 1, 2 and 4 was below
the EU average, while for Clusters 3 and 5 it was above that average. It is, therefore,
worth noting that the countries of the fourth cluster were characterized by relatively
poor performance in this area compared to the EU average;
•
Almost all countries in Cluster 1 and Cluster 2 (with the exception of Croatia and the
Czech Republic in Cluster 2) had sub-index value in this area below the EU average.
In the third cluster, it was only Austria, and in the fourth cluster as many as six out of
seven countries (the exception was the United Kingdom);
•
In 2020, the average value of indicators for this component in Clusters 1, 2 and 3 was
below the EU average, with lower values than the EU average for all countries in these
clusters except Croatia and the Czech Republic in Cluster 1 and France and Lithuania in
Cluster 3;
•
In 2020, in Clusters 4 and 5, the average value of the indicator in the cluster, as well
as the values of indicators for countries in these clusters, were higher than the EU-
28 average, with the exception of Estonia, Luxembourg and Germany in Cluster 4.
In all Clusters, the average rate in this area increased over the period 2015–2020,
with Cluster 4 (an increase of almost 101%) and Cluster 1 (more than 76%) and 5 (almost
46%) and Cluster 3 (an increase of 15%), and the smallest for Cluster 2 (less than 4%).
In the last area analyzed (
Digital Public Services
) a significant change in data is
visible for the two years covered by the survey. The average value of this indicator for the
EU-28 increased by 43.4%. In this respect, the change noted must be assessed positively,
as it demonstrates progress in the key element of the digital economy, which is digital
public services, and at the same time a condition necessary for strictly administrative
applications, such as:
•E-government users;
•Partially completed forms;
•Comprehensive online services, including business services;
•
The advancement of open data as a condition for greater participation of citizens in
such services, as well as the basis for the development of new ICT solutions;
•E-Health.
Energies 2021,14, 2717 18 of 26
An analysis of the country-specific changes recorded revealed that 14 out of 28 EU
countries achieved a below-average result in 2015 and 2020. In both years this was the
case in Bulgaria, Cyprus, the Czech Republic, Germany, Greece, Croatia, Hungary, Italy,
Romania, Slovenia, Slovakia and the United Kingdom and, in addition, in 2015, France and
Luxembourg, and, in 2020, Poland and Belgium. The lowest score in 2015 was recorded in
Greece (20.62 points), Croatia (27.14 points) and Hungary (28.21 points), while in 2020 in
Romania (48.41 points), Greece (51.50 points) and Slovakia (55.61 points).
The second half of EU countries achieved above-average results in the years analyzed.
These were Austria, Denmark, Estonia, Spain, Finland, Ireland, Lithuania, Latvia, Malta,
the Netherlands, Portugal, Sweden and, moreover, in 2015, Poland and Belgium, and,
in 2020, France and Luxembourg. Estonia (77.29 points), Denmark (74.38 points) and
Finland (72.12 points) scored the highest score in 2015, while Estonia (89.33 points) and
Denmark (87.13 points) and Spain (87.28) scored the highest score in 2020.
In relation to the EU-28 average (Table 1), it was found that:
•
In 2015, the lowest rate recorded for Greece was 41.07% of the EU-28 average and the
highest for Estonia was 153.93%;
•
In 2020, the lowest rate recorded for Romania was 67.23% of the EU-28 average and
the highest for Estonia was 124.08%. This clearly attests to the decreasing spread
of performance between EU countries in relation to the average (from 112.86 pps in
2015 to 56.84 pps in 2020), which promotes their convergence. The positive conver-
gence process is also confirmed by the improvement in the relationship between the
performance of the countries with the highest and lowest rates, as it has decreased
from 3.74 in 2015 to 1.85 in 2020. However, it is still substantially high.
The results of research in the area of Digital Public Services (DPS) in the cross-section
of isolated clusters have led to the following conclusions:
1.
For Cluster 1 in 2015, the rate was 43.09 points, 59.33 in 2020, an increase of 37.7%.
However, the achieved indicator values for Cluster 1 were below the UNION average;
2.
For Cluster 2 in 2015, the DPS was 27.92 points and in 2020 it was 63.24 points,
an increase of 126.5%. Despite such significant pro-growth developments, the DPS
achieved for this cluster was below the Union average;
3.
For Cluster 3 in 2015, the DPS was 62.26 points, then increased to 80.35 in 2020, i.e.,
by 29.0%, and its values in both 2015 and 2020 were above the Union average;
4.
For Cluster 4 in 2015, the DPS accounted for 50.92 points, after which it increased
to 75.82 in 2020, i.e., by 48.9%, and its values in both 2015 and 2020 were above the
Union average;
5.
For Cluster 5 in 2015, the DPS was 69.61 points, then increased to 83.61 in 2020, i.e.,
by 20.1%, and its values were both above the EU average in 2015 and 2020.
All clusters showed an increase in DPS. The most dynamic changes in the years
2020 and 2015 were compared for Cluster 2 countries, followed by Cluster 4 and Cluster 1,
and the smallest for Cluster 3 and Cluster 5.
Summarizing the above considerations and the effects of the empirical research under-
taken, it should be emphasized that, in the years 2015–2020, there were similarities between
individual EU countries in terms of the results achieved by them in the development of
the digital economy and society. The applied statistical methods in the field of structure
taxonomy allowed for the identification of a total of five cluster structures, among which
the lowest results in the development of the digital economy and society were achieved by
countries belonging to Cluster 1, and the highest to Cluster 5. On the basis of the conducted
research, it has been shown that there is a visible increase in convergence between the
EU-28 countries with regard to the digital economy and society, based on the main DESI
index and its four sub-indicators. For the Integration of Digital Technology sub-indicator,
an increasing polarization of results was demonstrated, which should be assessed as a
negative phenomenon. The authors of the paper would like to note the emerging need
for further research on this subject, with particular emphasis on Integration of Digital
Energies 2021,14, 2717 19 of 26
Technology in order to demonstrate the causes of this state and to formulate possible
proposals for changes for specific policies in the EU.
5. Discussion of Findings
Our results lead to an identification of 5 groups of the EU-28 countries according
to their levels of development of the digital economy and society. Thus, they prove the
research results observed in earlier studies indicating the fact of huge differentiation among
the EU countries within this issue [
23
,
68
,
84
]. On the basis of the data from 2015 and
2020, five cluster groups of countries were isolated basing on similar DESI values and the
similarity of values within its five internal structures. The analysis of the clusters shows
that their composition in 2020 compared to 2015 has changed significantly. Out of 28 EU
countries, 14 have remained in their clusters:
•Cluster 1: Bulgaria, Cyprus;
•Cluster 2: Hungary;
•Cluster 3: Austria, Spain, Lithuania;
•Cluster 4: Germany, Estonia, Luxembourg, the United Kingdom;
•Cluster 5: Denmark, Finland, the Netherlands and Sweden;
At the same time, other countries were promoted:
•from Cluster 1 to Cluster 2: Italy, Poland, Romania;
•from Cluster 3 to Cluster 4: Belgium, Ireland, Malta;
and others have moved to the lower cluster:
•from Cluster 4 to Cluster 3: France, Latvia, Slovenia;
•from Cluster 3 to Cluster 2: Portugal;
•from Cluster 2 to Cluster 1: The Czech Republic, Greece, Croatia, Slovakia.
We have found out that, in fact, the differences in the level of development of the digital
economy and society within the EU still exist, despite all the investments and policies made
in recent years. Moreover, the countries identified as the least digitally developed in earlier
studies remain basically the same, whereas the same refers to the most digitally developed
countries [
103
]. In our research, Bulgaria, Cyprus, Greece, Italy, Poland and Romania in
both years are among the two lowest clusters, while Denmark, Finland, the Netherlands
and Sweden are the digital leaders belonging to Cluster 5, which groups the most advanced
countries in this area. This situation is not surprising, considering that North European
Countries are pioneers in promoting digital development [
104
]. Our analyses show that,
in a synthetic assessment, all EU countries showed progress in the digital economy and
society, and the transition to a lower cluster concerned those countries that still showed
an increase in DESI and individual sub-rates in 2020 compared to 2015. On a cluster basis,
DESI and its five subcomponents reported positive dynamics of average sub-index values
for clusters, with the exception of Cluster 2, for which the average human capital ratio
decreased in 2020 compared to 2015. The results of our research, therefore, confirm that the
development of the digital economy and society of the Countries of the European Union is
not homogeneous and specific groups of countries (clusters) are distinguished, taking into
account the similarity of their performance in this area. The results obtained in this respect,
therefore, allow for positive verification of the hypothesis HS1.
Studies have shown that all EU countries have seen an increase in DESI during
the analysis period, which should clearly be assessed positively, but the scale of the
increases and their dynamics varied significantly. In relative terms, the greatest progress
(i.e., the greatest dynamics of change) in 2020 compared to 2015 was shown among countries
making up for the development gap in relation to the EU average, such as Hungary, Romania,
Italy, while the lowest dynamics were in Denmark, Estonia and Sweden. Our research has also
shown that in the five subcomponents that make up the overall DESI index, the performance
of individual countries has improved, with different dynamics of change taking place in
different areas. The only exception here is Latvia, whose human capital ratio has deteriorated.
Energies 2021,14, 2717 20 of 26
In the area of connectivity, taking into account the average value of the indicator for
the EU-28 countries, the most dynamic developments have been recorded, which should
be assessed highly positively, as this shows a dynamic improvement in the connectivity
situation, both from the demand side and the supply of fixed and mobile broadband.
In the area of human capital, it was found that all EU countries except Latvia had seen
an improvement in the index, but the dynamics of change here were significantly lower
than for the other indicators as well as the synthetic DESI. The changes taking place in this
area are, therefore, much slower, as they concern changes in the knowledge and skills of
nationals and are the result of existing ICT education solutions in those countries. They are,
therefore, evolutionary rather than revolutionary that was also observed in research of
others, [
105
]. The third indicator, for the Use of Internet services area, was relatively high
in the years analyzed compared to the other components, and its average value for the
EU-28 was second only to those in the area of Digital Public services. However, the growth
rate of the EU-28 average was lower than the DESI synthetic index and connectivity,
integration of digital technology and digital public services indicators. On the other hand,
the dynamics of the average component 4 indicator (Integration of Digital Technology)
have only given way to the dynamics of change in connectivity and Digital Public services
over the years, which demonstrates the dynamic development of digital technologies and
e-commerce channels in the business sector in the European Union. The fifth area examined,
Digital Public services, was the second-highest growth rate for the EU-28, indicating the
rapidly growing potential of ICT in the public sector, both in demand and in the supply of
digital public services and in open data.
Finally, when analyzing the DESI synthetic index and its components, it was concluded
that there is generally an increase in the convergence between the EU-28 countries in
relation to the economy and the digital society over the years analyzed, however in some
dimensions there is evidence that the European digital divide is narrowing, while in the
other it appears to be widening. This is evidenced by the positive changes in the relationship
between the performance of the countries with the highest and lowest rates: synthetic and
in the areas of Connectivity, Human Capital, Use of Internet and Digital Public Services,
both in relation to each other and in relation to the EU-28 average. The largest reductions
in the range of results for countries with extreme indicator values in 2015 and 2020 were
recorded in the areas of Digital Public Services, Use of the Internet and Connectivity.
It can, therefore, be concluded that these are the areas where the least developed countries
in a given respect make the most progress compared to the leaders’ countries and the
EU-28 average. Dynamic positive changes in the indicated areas are also confirmed by
studies by other authors [
68
,
106
]. Only a slight increase in the convergence between the
EU-28 countries has been recorded in terms of Human Capital, which only confirms the fact
that changes are taking place here relatively more slowly than in other areas. Noteworthy
is the fact that the area of Integration of Digital Technology was the only one where there
was an increasing polarization of results. Unlike other components, in terms of the use
of digital technologies, the differences between EU countries widened over the period
analyzed; the gap between the lowest and highest performing countries in relation to
the EU-28 average increased, and the advantage of the country with the highest score in
relation to the worst-performing country also increased. Our findings in this respect are,
therefore, in line with those of other authors, indicating that the transition to a digital
economy and European society is gradual in all sectors, and its rhythm is dictated by the
individual initiatives of the Member States and the corresponding historical economic
sectors. European and national initiatives involving both private and public investment in
the deployment of new digital technologies provide diverse opportunities for the industry
in particular countries to achieve new benchmarks [107].
The results presented clearly confirm that the levels of indicators and the dynamics
of changes in their values in 2015 and 2020 in the five baseline areas of the DESI analysis
vary despite the fact that almost all of them are pro-growth, including for isolated clusters
of EU-28 countries, and the pace of their changes is not uniform, which allows positive
Energies 2021,14, 2717 21 of 26
verification of the hypotheses HS2 and the main hypothesis. However, the direction and
dynamics of the changes in indicators across countries indicate different convergence
processes in the synthetically understood area of the digital economy and society, as well as
in terms of connectivity, human capital, internet use and digital public services. Only in the
area of the use of digital technologies have the differences between EU countries widened
during the analysis period. The analyses presented are an indication of the European Union
administration’s efforts to promote the sustainable development of the digital economy
and society. They should serve as an indication of the increase in national expenditure on
the development of individual components, especially those with a weaker performance of
the particular country in comparison with other EU countries.
6. Conclusions
The results of the analysis of digitization processes and their effects in the field of
Connectivity, Human Capital, Use of Internet, Integration of Digital Technology and Digital
Public Services are important for shaping the proper domestic policy of governments,
for strengthening the competitiveness of a given country in various areas of the economy
and for everyday life. In 2014 the European Commission introduced the Digital Economy
and Society Index (DESI). It is a comprehensive measure for the synthesis of data received
from individual member countries. The indicator is an effective tool for assessing the
economic and social impact of digitization in the EU. Moreover, in recent years DESI
has served as an important factor in shaping European policy on harmonious digital
development. It is also a reference point for the comparative analyzes of various aspects of
digitization in individual EU countries.
In this study, DESI was used for the complete and up-to-date analysis of the economic
and social aspects of digitization in the 28 EU countries. It was done in the context of the
trend of digital convergence. The results of the analyzes performed can help in shaping
the economic policy in Europe. They show the strengths and weaknesses of the digital
development strategy in individual EU countries. They also point to those areas where
the difference in the dynamics of digitization between more and less digitally advanced
countries still remains very noticeable.
The research presented here also has some limitations. Although DESI is a widely
used and effective measure of digitization processes, this indicator should be used with
some caveats. Its introduction was aimed at providing a unified and standardized image of
the comprehensive digital ecosystem that forming inside the EU. The methodology applied
here was subject to the requirement of universality, which was aimed to make it effective
in research conducted in an individual EU country. For this reason, the obtained results are
general in nature and require deeper analysis. This implies the need to collect additional,
including qualitative, data to explain certain state-specific phenomena. Another issue that
should be noted here is the difference between individual statistical offices and between
the methods of collecting data in individual EU countries.
Despite the limitations outlined above, DESI can be regarded as an effective tool for
drawing up a comprehensive picture of the complex dynamics of digitization processes in
today’s Europe. Digital convergence still remains an open problem, which implies the need
to deepen the research presented in this paper. Although the analysis of individual clusters
presented in the paper may be useful for the quantitative assessment of differences in the
dynamics of digitization processes in individual countries, our study does not explain the
reason for the existence of such differences. The presented analysis focuses on five main
dimensions that make up DESI: Connectivity, Human Capital, Use of Internet, Integration
of Digital Technology and Digital Public Services. A more detailed picture of the dynamics
of digital development in future research can be obtained by referring to aspects of the
process that are more specific than those mentioned above, using second-level data for each
dimension. This would allow, inter alia, to explain the existence of significant differences
in the dynamics of digitization between individual EU countries.
Energies 2021,14, 2717 22 of 26
Author Contributions:
Conceptualization, R.B., J.M., M.S., A.T., K.˙
Z. Formal analysis, B.S.-T., A.T.,
K. ˙
Z. Investigation, B.S.-T., A.T., K. ˙
Z. Methodology, M.S.; Resources, K. ˙
Z.; Writing—original draft,
B.S.-T., A.T., K. ˙
Z. Writing—review & editing, R.B., J.M. All authors have read and agreed to the
published version of the manuscript.
Funding:
The publication is financed by the subsidy granted to the Cracow University of Economics
and WSB University in D ˛abrowa Górnicza. This publication is a component of scientific research
carried out at the University of Economics in Cracow as a part of the research of the Potential Program,
WSB University in D ˛abrowa Górnicza as a part of the research carried out at this University and the
Faculty of Management of AGH University for the maintenance and development of research potential.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: Publicly available datasets were analyzed in this study. This data can
be found here: https://digital-agenda-data.eu/ (accessed on 23 October 2020).
Conflicts of Interest: The authors declare no conflict of interest.
Appendix A
Table A1. Value of DESI and its subcomponents in 2015 in a cross-section of EU-28 countries *.
Country DESI Connectivity Human Capital Use of Internet
Services
Integration of Digital
Technology Digital Public Services
Austria 40,54 28,26 50,19 42,20 28,02 59,93
Belgium 42,82 31,44 46,38 47,19 42,84 51,46
Bulgaria 26,81 26,10 29,18 29,51 15,28 36,73
Croatia 33,00 22,60 41,00 39,20 35,74 27,14
Cyprus 30,65 17,44 33,55 37,48 25,00 48,52
Czech Republic 38,10 32,38 44,86 42,60 37,20 33,04
Denmark 56,98 45,11 60,63 65,31 47,96 74,38
Estonia 48,69 38,69 55,89 56,73 24,71 77,29
Finland 56,67 39,64 72,21 63,11 42,13 72,12
France 37,46 30,63 44,26 44,57 25,91 45,80
Germany 41,08 35,70 51,26 48,66 28,48 42,31
Greece 26,13 18,93 34,21 32,67 24,27 20,62
Hungary 31,69 29,40 38,44 46,03 17,95 28,21
Ireland 43,07 26,29 47,46 41,49 46,29 61,01
Italy 29,75 23,58 30,81 32,08 22,18 46,04
Latvia 38,62 45,52 36,73 45,04 15,53 54,64
Lithuania 40,37 34,33 34,94 48,15 33,68 60,65
Luxembourg 44,77 45,10 52,57 58,22 26,90 41,58
Malta 46,49 32,73 53,36 47,70 35,65 71,20
Netherlands 51,23 40,37 54,33 59,55 43,38 66,31
Poland 31,31 27,84 31,68 33,42 17,27 53,06
Portugal 37,08 30,85 31,91 34,87 30,12 67,55
Romania 26,87 34,94 27,47 21,71 16,73 31,11
Slovenia 38,23 36,28 41,66 43,41 26,67 46,02
Slovakia 33,06 28,88 36,97 42,78 27,95 30,60
Spain 41,36 33,54 43,20 44,77 29,27 64,06
Sweden 55,13 44,80 61,26 65,07 45,05 65,64
United Kingdom 45,70 34,23 56,35 58,25 35,00 48,80
EU-28 38,91 31,13 43,99 45,01 29,22 50,21
* Values rounded to two decimal places. Source: own study based on European Commission data available on the https://digital-agenda-
data.eu/ (accessed on 23 October 2020).
Energies 2021,14, 2717 23 of 26
Table A2. DESI and its subcomponents in 2020 in a cross-section of EU-28 countries *.
Country DESI Connectivity Human Capital Use of Internet
Services
Integration of Digital
Technology
Digital Public
Services
Austria 54,31 47,15 56,73 54,02 40,58 80,84
Belgium 58,71 52,03 50,39 61,16 65,87 71,73
Bulgaria 36,44 38,50 33,92 36,65 17,86 61,76
Croatia 47,56 41,15 49,15 55,48 41,47 55,75
Cyprus 43,98 38,46 35,80 54,48 34,48 68,96
Czech Republic 50,78 44,88 48,64 54,14 49,59 62,38
Denmark 69,15 65,82 61,28 75,15 65,15 87,13
Estonia 61,07 51,86 66,66 65,42 41,15 89,33
Finland 72,31 59,17 78,44 76,34 67,05 86,99
France 52,19 49,84 47,43 53,06 42,05 76,71
Germany 56,05 59,40 56,42 61,57 39,53 66,37
Greece 37,32 33,37 34,79 46,09 28,20 51,50
Hungary 47,52 59,79 41,84 55,90 25,31 57,77
Ireland 61,79 45,69 56,40 62,09 74,32 80,63
Italy 43,65 49,99 32,46 44,48 31,23 67,48
Latvia 50,71 61,76 35,02 53,98 28,31 85,06
Lithuania 53,89 48,88 43,83 57,32 49,47 81,45
Luxembourg 57,92 63,35 58,22 58,85 38,21 73,74
Malta 62,70 58,72 61,76 65,90 54,90 78,13
Netherlands 67,69 60,32 64,16 75,20 65,75 80,96
Poland 44,96 51,34 37,27 49,64 26,24 67,41
Portugal 49,58 53,92 37,77 48,09 40,87 75,12
Romania 39,97 56,19 33,16 35,89 24,93 48,41
Slovenia 51,20 50,23 48,35 51,71 40,95 70,75
Slovakia 45,18 47,46 41,81 53,36 32,57 55,61
Spain 57,54 60,79 47,56 60,78 41,22 87,28
Sweden 69,74 64,37 71,72 75,95 62,13 79,34
United Kingdom 60,41 48,82 63,00 73,31 54,19 70,77
EU-28 52,62 50,06 49,29 58,03 41,37 72,00
* Values rounded to two decimal places. Source: own study based on European Commission data available on the https://digital-agenda-
data.eu/ (accessed on 23 October 2020).
References
1. Iersel, J.P. Europe 2020 and the future of the European Union. Dans Eur. Format. 2011,4, 147–168. [CrossRef]
2. Martens, W. Europe 2020 and beyond. Eur. View 2010,9, 1–3. [CrossRef]
3. Hoedl, E. Europe 2020 strategy and European recovery. Probl. Sust. Dev. 2011,6, 11–18.
4. Lundvall, B.A.; Lorenz, E. From the Lisbon strategy to Europe 2020. In Towards Social Investment Welfare State? Ideas, Policies and
Challenges; Morel, N., Palier, B., Palme, J., Eds.; Policy Press: Bristol, UK, 2016; pp. 333–352.
5.
Siuta-Tokarska, B.; Their, A.; ˙
Zmija, K. Procesy i Problemy w Realizacji Zrównowa˙
zonego i Trwałego Rozwoju w Polsce. Kontekst
mikroekonomiczny (Processes and Problems in the Implementation of Sustainable Development in Poland. Microeconomic Context); PWN:
Warszawa, Poland, 2019; pp. 11–28.
6.
Portillo, A.F.; Gonzalez, M.A.; Mogollon, R.H. Impact of ICT development on economic growth. A study of OECD European
union countries. Technol. Soc. 2020,63. [CrossRef]
7.
Das, A.; Khan, S.M. Effects of ICT development on economic growth in emerging Asian countries. In ICT in Developing Countries.
Research, Practices and Policy Implications; Day, B., Sorour, K., Filieri, R., Eds.; Palgrave Macmillan: London, UK, 2016; pp. 141–159.
8.
Day, B.; Ali, F. A critical review of the ICT for development research. In ICT in Developing Countries. Research, Practices and Policy
Implications; Day, B., Sorour, K., Filieri, R., Eds.; Palgrave Macmillan: London, UK, 2016; pp. 3–23.
Energies 2021,14, 2717 24 of 26
9.
Vicente, M.R.; Gil-de-Bernabe, F. Assessing the broadband gap: From the penetration divide to the quality divide. Technol.
Forecast. Soc. Chang. 2010,77, 816–822. [CrossRef]
10.
Vicente, M.R.; Lopez, A.J. A multidimensional analysis of the disability digital divide: Some evidence for internet use. Inf. Soc.
Int. J. 2010,26, 48–64. [CrossRef]
11.
Cilan, A.; Bolat, B.A.; Coskun, E. Analyzing digital divide within and between member and candidate countries of European
Union. Gov. Inf. Q. 2009,26, 98–105. [CrossRef]
12.
Dro˙
zd˙
z, W.; Mi´skiewicz, R.; Pokrzywniak, J.; El˙
zanowski, F. Urban. Electromobility in the Context of Industry 4.0; Wydawnictwo
Adam Marszałek: Toru´n, Poland, 2019.
13. Selvin, N.; Facer, K. Beyond the Digital Divide: Rethinking Digital Inclusion for the 21st Century; Futurelab: Bristol, UK, 2007; p. 40.
14. Valenduc, G.; Vendramin, P. Work in the Digital Economy: Sorting the Old from the New; ETUI aisbl: Brussels, Belgium, 2016.
15.
Servoz, M. AI. The Future of Work? Work of the Future! On How Artificial Intelligence, Robotics and Automation Are Transforming Jobs
and the Economy in Europe; European Commission: Brussels, Belgium, 2019.
16.
Brynjolfsson, E.; McAfee, A. Machine, Platform, Crowd: Harnessing Our Digital Future; W.W. Norton & Company: New York, NY,
USA, 2017.
17.
Brooks, S.; Donovan, P.; Rumble, C. Developing nations. The digital divide and research databases. Ser. Rev.
2005
,31, 270–278.
[CrossRef]
18.
Qiang, C.Z.; Rossotto, C.M. Information and Communications for Development 2009: Extending Reach and Increasing Impact; World
Bank: Washington, DC, USA, 2009; pp. 35–50.
19.
Koutroumpis, P. The economic impact of broadband on growth: A simultaneous approach. Telecommun. Policy
2009
,33, 471–485.
[CrossRef]
20.
Czernich, N.; Falck, O.; Kretschmer, T.; Woessmann, L. Broadband infrastructure and economic growth. Econ. J.
2011
,121, 505–532.
[CrossRef]
21.
Kongaut, C.; Bohlin, E. Impact of broadband speed on economic outputs: An empirical study of OECD countries. In Proceedings of
the 25th European Regional Conference of the International Telecommunications Society (ITS), Brussels, Belgium, 22–25 June 2014.
22. Van Dijk, J.; Hacker, K. The digital divide as a complex and dynamic phenomenon. Inform. Soc. 2003,19, 315–326. [CrossRef]
23. Cruz-Jesus, F.; Oliveira, T.; Bacao, F. Digital divide across the European Union. Inf. Manag. 2012,4, 278–291. [CrossRef]
24.
Fors, M.; Moreno, A. The benefits and obstacles of implementing ICT strategies for development from a bottom-up approach.
Aslib Proc. 2002,54, 198–206. [CrossRef]
25.
Kuriyan, R.; Ray, I.; Toyama, K. Information communication technologies for development: The bottom of the pyramid model in
practice. Inf. Soc. 2008,24, 1–12. [CrossRef]
26. Mariscal, J. Digital divide in a developing country. Telecommun. Policy 2005,29, 409–428. [CrossRef]
27.
Parmar, V.; Keyson, D.; de Bont, C. International federation for information processing shaping social belief: A community
sensitive health information system for rural India. In Home Informatics and Telematics: ICTs for the Next Billion; Venkatesh, A.,
Gonzalves, T., Monk, A., Buckner, K., Eds.; Springer: Boston, MA, USA, 2007; pp. 133–144.
28.
Donner, J. Research approaches to mobile use in the developing world: A review of the literature. In Proceedings of the
International Conference on Mobile Communication and Asian Modernities, Hong Kong, China, 7–8 June 2005.
29.
Rashid, A.T.; Elder, L. Mobile phones and development: An analysis of IDRC—Upported projects. Electron. J. Inf. Syst. Dev. Ctries.
2009,36, 1–16. [CrossRef]
30.
Thapa, D.; Sæbø, Ø. Exploring the link between ICT and development in the context of developing countries: A literature review.
Electron. J. Inf. Syst. Dev. Ctries. 2014,64, 1–15. [CrossRef]
31.
Van Dijk, J.A.G.M. The Evolution of the Digital Divide. The Digital Divide Turns to Inequality of Skills and Usage, Digital Enlightenment
Yearbook; IOS Press: Amsterdam, The Netherlands, 2012; pp. 57–78.
32.
Jabło´nski, A.; Jabło ´nski, M. Social perspectives in digital business models of railway enterprises. Energies
2020
,13, 6445. [CrossRef]
33.
Pelau, C.; Acatrinei, C. The paradox of energy consumption decrease in the transition period towards a digital society. Energies
2019,12, 1428. [CrossRef]
34.
Chen, S.; Zhou, M. Evolving container to Unikernel for edge computing and applications in process industry. Processes
2021
,9, 351.
[CrossRef]
35.
Kling, R.; Lamb, R. IT and organizational change in digital economies. In Understanding the Digital Economy; Brynjolfsson, E.,
Kahin, B., Eds.; MIT Press: Cambridge, MA, USA, 2000; pp. 295–324.
36. Dahlman, C.; Mealy, S.; Wermelinger, M. Harnessing the Digital Economy for Developing Countries; OECD: Paris, France, 2016.
37. Tapscott, D. The Digital Economy: Promise and Peril in the Age of Networked Intelligence; McGraw-Hill: New York, NY, USA, 1996.
38. Lane, N. Advancing the digital economy into the 21st century. Inf. Syst. Front. 1999,1, 317–320. [CrossRef]
39.
Brynjolfsson, E.; Kahin, B. (Eds.) Understanding the Digital Economy: Data, Tools, and Research; MIT Press: Cambridge, MA, USA, 2000.
40. Mesenbourg, T.L. Measuring the Digital Economy; US Bureau of the Census: Suitland, MD, USA, 2001.
41. Knickrehm, M.; Berthon, B.; Daugherty, P. Digital Disruption: The Growth Multiplier; Accenture: Dublin, Ireland, 2016.
42.
Domazet, I.; Zubovi´c, J.; Lazi´c, M. Driving factors of Serbian competitiveness: Digital economy and ICT. Strateg. Manag.
2018
,23,
20–28. [CrossRef]
43.
Bukht, R.; Heeks, R. Defining, Conceptualising and Measuring the Digital Economy; University of Manchaster: Manchaster, UK, 2017;
pp. 4–10.
Energies 2021,14, 2717 25 of 26
44.
A Roadmap toward a Common Framework for Measuring the Digital Economy, Report for the G20 Digital Economy Task Force; OECD:
Nejla Saula, Saudi Arabia, 2020.
45.
Preparing the Future of Work We Want: The Digital Economy and Labour Skills and Competences. In Proceedings of the 19th
American Regional Meeting, Panama City, Panama, 2–5 October 2018; International Labour Organization: Geneva, Switzerland,
2018. Available online: https://www.ilo.org/wcmsp5/groups/public/---ed_norm/---relconf/documents/meetingdocument/
wcms_644863.pdf (accessed on 5 January 2021).
46. The Digital Economy for Structural Change and Equality; ECLAC: Santiago, Chile, 2013.
47. Katzenbach, C.; Bächle, T.C. Defining concepts of the digital society. Internet Policy Rev. 2019,8. [CrossRef]
48. Isin, E.; Ruppert, E. Being Digital Citizens; Rowman & Littlefield Publishers: London, UK, 2015.
49.
Sandulescu Budea, A.M. An approximation in the study of communication research: Digital evolution and the study of this
subject in spanish academic journals. In Improving University Reputation through Academic Digital Branding; Del Pino, A.D., Romero,
N.L., Eds.; IGI Global: Hershey, PA, USA, 2021.
50.
Finos, R. 2016–2026 Worldwide Big Market Forecast, Wikibon 2016. Available online: https://wikibon.com/2016-2026-
worldwide-big-data-market-forecast/ (accessed on 5 January 2021).
51.
Efimova, A.; Ruchkina, M.O.; Tereshina, O.Y. Transformation of the energy sector in conditions of digital economy. Light Eng.
2018,26, 69–75.
52.
Vlasov, A.I.; Shakhnov, V.A.; Filin, S.S.; Krivoshein, A.I. Sustainable energy systems in the digital economy: Concept of smart
machines. Entrep. Sustain. Issues 2019,6, 1975–1986. [CrossRef]
53.
Avdeeva, E.; Davydova, T.; Belyantseva, O.; Makeeva, T. Factors of Sustainable Development of Energy Technologies in the
Formation of Digital Economy. In Proceedings of the E3S Web Conference “Topical Problems of Green Architecture, Civil and
Environmental Engineering” (TPACEE 2019), Moscow, Russia, 19–22 November 2019. [CrossRef]
54.
Morishita-Steffen, N.; Alberola, R.; Mougeot, B.; Vignali, E.; Wikström, C.; Montag, U.; Gastaud, E.; Lutz, B.; Hartmann, G.;
Pfaffenbichler, F.X.; et al. Smarter together: Progressing smart data platforms in Lyon, Munich, and Vienna. Energies
2021
,14, 1075.
[CrossRef]
55.
Borowski, F. Innovative Processes in Managing an Enterprise from the Energy and Food Sector in the Era of Industry 4.0. Processes
2021,9, 381. [CrossRef]
56.
Balcerzak, A.P. Technological potential of European Economy. Proposition of measurement with application of multiple criteria
decision analysis. Montenegrin J. Econ. 2016,12, 7–17. [CrossRef]
57.
Stanickova, M. Can the implementation of the Europe 2020 Strategy goals be efficient? The challenge for achieving social equality
in the European Union. Equilibr. Q. J. Econ. Econ. Policy 2017,12, 383–398. [CrossRef]
58. Europe 2020—A Strategy for Smart, Sustainable and Inclusive Growth; European Commission: Brussels, Belgium, 2010; p. 37.
59. A Digital Agenda for Europe; European Commission: Brussels, Belgium, 2010.
60.
Francik, A.; Szczepa´nska-Woszczyna, K. Innowacje i Przedsi˛ebiorczo´s´c—Uj˛ecie Makro- i Mikroekonomiczne (Innovation and
Entrepreneurship—A
Macro- and Micro-Economic Perspective); Wy˙
zsza Szkoła Biznesu w D ˛abrowie Górniczej: D ˛abrowa Górnicza, Poland, 2016.
61.
Borowiecki, R.; Olesi´nski, Z.; Rzepka, A.; Hys, K. Development of teal organisations in Economy 4.0. An empirical research. Eur.
Res. Stud. J. 2021,24, 117–129. [CrossRef]
62.
MEMO/10/200, Europejska Agencja Cyfrowa: Kluczowe Inicjatywy (European Digital Agenda: The Key Initiatives). Available
online: https://ec.europa.eu/commission/presscorner/detail/pl/MEMO_10_200 (accessed on 1 February 2021).
63. A Digital Single Market. Strategy for Europe; European Commission: Brussels, Belgium, 2015.
64.
Report on the Safety and Liability Implications of Artificial Intelligence, the Internet of Things and Robotics; European Commission:
Brussels, Belgium, 2020.
65. Europe’s Moment: Repair and Prepare for the Next Generation; European Commission: Brussels, Belgium, 2020.
66.
Beck, K.; Grodzicki, M. Konwergencja Realna i Synchronizacja Cykli Koniunkturalnych w Unii Europejskiej. Wymiar Strukturalny (Real
Convergence and Synchronization of Business Cycles in the European Union. Structural Dimension); Wyd. Scholar: Warszawa, Poland,
2014; p. 10.
67. Iosifidis, P. Digital convergence: Challenges for European regulation. J. Eur. Inst. Commun. Cult. 2002,9, 27–47. [CrossRef]
68.
Kyriakidou, V.; Michalakelis, C.; Sphicopoulos, T. Digital divide gap convergence in Europe. Technol. Soc.
2011
,33, 265–270.
[CrossRef]
69.
Hsu, W.C.H.J.; Tsai, M.H.; Tzeng, G.H. Exploring the best strategy plan for improving the digital converegence by using a hybrid
MADM model. Technol. Econ. Dev. Econ. 2018,24, 164–198. [CrossRef]
70. Lindgren, S. Digital Media and Society; SAGE: London, UK, 2017.
71. Papacharissi, Z.A. A Private Sphere. Democracy in a Digital Age; Polity Press: Cambridge, MA, USA, 2010.
72.
Fagerjord, A.; Storsul, T. Questioning convergence. In Ambivalence towards Convergence. Digitalization and Media Change; Storsul, T.,
Stuedahl, D., Eds.; Nordicom, University of Gothenburg: Gothenburg, Sweden, 2007; pp. 19–31.
73.
Baldwin, T.F.; Stevens McVoy, D.; Steinfield, C. Convergence: Integrating Media, Information and Communication; Sage: London, UK, 1996.
74.
Green Paper on Convergence of the Telecommunications, Media and Information Technology Sectors, and the Implications for Regulation–
Towards an Information Society Approach; European Commission: Brussels, Belgium, 1997.
75.
Fagerjord, A. Reading-view(s)ing the Über-box: A critical view on a popular prediction. In Cybertext Yearbook 2001; Eskelinen, M.,
Koskimaa, R., Eds.; Publications of the Research Centre for Contemporary Culture, University of Jyväskylä: Jyväskylä, Finland, 2002.
Energies 2021,14, 2717 26 of 26
76. Jenkins, H. Convergence Culture: Where Old and New Media Collide; MIT Press: Cambridge, MA, USA, 2006.
77.
Beyer, Y.; Enli, G.; Maasø, A.; Ytreberg, E. Small talk makes a big difference: Recent developments in interactive SMS-based TV.
Television New Media 2007,3. [CrossRef]
78. Picard, R. The Economics of Financing of Media Companies; Fortham University Press: New York, NY, USA, 2002.
79. Hoskins, C.; McFaiden, S.; Finn, A. Media Economics: Applying Economics to New and Traditional Media; Sage: London, UK, 2004.
80.
Østergaard, B.S. Convergence: Legislative dilemmas. In Media Policy. Convergence, Concentration and Commerce; McQuail, D.,
Siune, K., Eds.; Sage: London, UK, 1998.
81.
Cuilenburg, J.; Slaa, P. From media policy towards a national communications policy: Broadening the scope. Eur. J. Commun.
1993,8, 149–176. [CrossRef]
82.
Skogerbø, E. Konvergens Mellom Telekommunikasjon og Kringkasting. Kulturogmediepolitiske Utfordringer; TemaNord, Nordic Council
of Ministers: Copenhagen, Denmark, 1997; p. 560.
83.
Noveck, B.S. Thinking analogue about digital television? Bringing European content regulation into the information age.
In Convergence in European Digital TV Regulation; Marsden, C., Verhulst, S., Eds.; Blackstone Press Limited: London, UK, 1999.
84. Mitrovic, D. Measuring the efficiency of digital converegence. Econ. Lett. 2020,188, 1–3. [CrossRef]
85.
Laitsou, E.; Kargas, A.; Varoutas, D. Digital competitiveness in the European Union era: The Greek case. Economies
2020
,8, 85.
[CrossRef]
86. Kotarba, M. Measuring digitalization—Key metrics. Found. Manag. 2017,9, 123–138. [CrossRef]
87.
Chaaben, N.; Faysal, M. Digital economic and social evolution of Tunisia. In Proceedings of the Management International
Conference, Venice, Italy, 24–27 May 2017; pp. 393–404.
88.
Ziemkiewicz, B.; Pietrzak, M. Digital economy in the old European Union Member States. In Proceedings of the 18th International
Scientific Conference Globalization and Its Socio-Economic Consequences, Rajecke Teplice, Slovakia, 10–11 October 2018; pp.
2437–2444.
89. Digital Economy and Society Index (DESI) 2020, Methodological Note; European Commission: Brussels, Belgium, 2020; pp. 4–14.
90.
Vidruska, R. The Digital Economy & Society Index and Network Readiness Index: Performance of Latvia on European Union
Arena. In Proceedings of the New Challenges of Economic and Business Development Conference, Riga, Latvia, 12–14 May 2016;
University of Latvia: Riga, Latvia, 2016. Available online: https://www.academia.edu/27238130/The_Digital_Economy_and_
Society_Index_and_Network_Readiness_Index_performance_of_Latvia_on_European_Union_arena (accessed on 23 March 2021).
91.
Nagy, S. Digital economy and society. A cross country comparison of Hungary and Ukraine. Biсник НaцioнaльнoгoТехнiчнoгo
Унiверситету“ХaркiвськийΠoлiтехнiчний Iнститут” (Екoнoмiчнi Нaуки)2017,46, 174–179.
92.
Moroz, M. The level of development of the digital economy in Poland and selected european countries: A comparative analysis.
Found. Manag. 2017,9, 175–190. [CrossRef]
93.
Balacescu, A.; Babucea, A.G. Use of the internet by the Romanian citizens. An empirical study of digital gaps between regions.
Analele Univ. Constantin Brâncu Târgu Jiu Ser. Econ. 2018,1, 81–88.
94. Cesnauske, J. Digital economy and society: Baltic states in the EU context. Econ. Cult. 2019,16, 80–90. [CrossRef]
95.
Stavytskyy, A.; Kharlamova, G.; Stoica, E.A. The analysis of the digital economy and society index in the EU. Balt. J. Eur. Stud.
2019,9, 245–261. [CrossRef]
96. International Digital Economy and Society Index 2020; European Union: Brussels, Belgium, 2021; p. 6.
97.
Soszy´nska, E. Konwergencja technologiczna a potencjał społeczno-technologiczny krajów Unii Europejskiej (Technological
convergence and socio-technological potential of European Union countries). Metod. Ilo´sciowe Bad. Ekon. (Quant. Methods Econ.
Res.) 2012,18, 191–201.
98.
Digital Economy and Society Index, Eurostat. Available online: https://digital-agenda-data.eu/datasets/desi/visualizations
(accessed on 23 October 2020).
99. Everitt, B.S.; Landau, S.; Leese, M. Cluster Analysis; Oxford University Press: London, UK, 2001.
100. Ward, J.H., Jr. Hierarchical grouping to optimize an objective function. J. Am. Stat. Assoc. 1963,58, 236–244. [CrossRef]
101.
Stanisz, A. Przyst˛epny kurs Statystyki z Zastosowaniem STATISTICA PL na Przykładach z Medycyny. Tom 3 pt.: Analizy Wielowymiarowe
(An Accessible Course in Statistics Using STATISTICA PL with Examples from Medicine. Volume 3: Multivariate Analyses); StatSoft
Polska Sp. z o.o.: Kraków, Poland, 2007.
102. Kaufman, L.; Rousseeuw, P.J. Finding Groups in Data: An Introduction to Cluster Analysis; Wiley: New York, NY, USA, 1990.
103.
Bánhidi, Z.; Dobos, I.; Nemeslaki, A. What the overall digital economy and society index reveals: A statistical analysis of the
DESI EU28 dimensions. Reg. Stat. 2020,10, 42–62. [CrossRef]
104.
Castells, M.; Himanen, P. The Information Society and the Welfare State: The Finnish Model; Oxford University Press: New York, NY,
USA, 2002.
105.
Grigorescu, A.; Pelinescu, E.; Ion, A.E.; Dutcas, M.F. Human capital in digital economy: An empirical analysis of central and
eastern European countries from the European Union. Sustainability 2021,13, 2020. [CrossRef]
106.
Simicevic, V.; Zoroja, J.; Pejic Bach, M. Cluster analysis of interent usage in European countries. In Proceedings of the Annals of
DAAAM for 2012 & Proceedings of the 23rd International DAAAM Symposium “Intelligent Manufacturing & Automation”, Zadar,
Croatia, 24–27 October 2012; Katalinic, B., Ed.; DAAAM International: Vienna, Austria, 2012; Volume 23, No.1. pp. 525–528.
107. ¸Schiopu, R. Business digitization in the European Union. Proc. Int. Conf. Bus. Excell. 2020,14, 385–397. [CrossRef]
Content uploaded by Katarzyna Żmija
Author content
All content in this area was uploaded by Katarzyna Żmija on May 12, 2021
Content may be subject to copyright.
