Stankovic, J. J., Marjanovic, I., Drezgic, S., & Popovic, Z. (2021).

The Digital Competitiveness of

European Countries: A Multiple-Criteria Approach. Journal of Competitiveness, 13(2), 117–134. https://doi.
org/10.7441/joc.2021.02.07

The Digital Competitiveness of European Countries:

A Multiple-Criteria Approach
▪▪ Jelena J. Stankovic, Ivana Marjanovic, Sasa Drez gic, Zarko Popovic
Abstract
High-quality digital infrastructure is the basis of almost every sector of a modern and innovative
economy and society. As a part of the overall competitiveness concept, digital competitiveness
is a multidimensional structure that encompasses various factors of the process of digital
transformation through the ability of learning and application of new technologies, technology
factors that enable digital transformation, and digital readiness factors that assess the preparedness
of an economy and citizens to assume digital transformation. The paper aims to propose a
methodology for measuring digital competitiveness using a composite index approach including
a variety of various indicators. To assess the digital competitiveness of European countries, a
multi-criteria analysis was applied in a two-stage procedure integrating CRITIC and TOPSIS
as weighting and aggregation methods. The sample includes thirty European countries and the
research is based on thirteen indicators provided in the database Eurostat Digital Economy
and Society. In addition, a ranking of sample countries according to digital competitiveness
is presented. Finally, a cluster analysis was conducted to examine relations between digital
competitiveness and several economic performances such as GDP pc, labour productivity and
employment rates. The results indicate that Nordic countries have achieved the highest digital
competitiveness, while most Eastern European countries still lag behind.

Keywords: digital competitiveness, CRITIC method, TOPSIS method, cluster analysis

JEL Classification: C38, C44, O52, L86

Received: October, 2020

1st Revision: April, 2021
Accepted: April, 2021

1. INTRODUCTION
Constant technological progress and the constant acceleration of the pace of technological
change have become basic features in countries around the world. According to projections,
by the end of 2020, one million new devices were set be available online every hour (Yoo et al.,
2018). The impact of the Internet of Things and digitization is pervasive. The application of ICTs
(information and communication technologies) can transform the way businesses operate and how
people live as well as drive global innovation. However, the rapid emergence of new technologies
creates many new challenges. The risks inherent in new technologies further complicate the

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 problems facing policymakers. The role of government is becoming more and more important,
as it is necessary to strike a balance between protecting the country’s fundamental interests
on the one hand and the ability to ensure national competitiveness and accelerate economic
growth on the other through the use of new technologies. There is evidence that digitization
can enable countries to maintain global competitiveness, increase GDP, stimulate innovation
and create jobs (Yoo et al., 2018). It is recognized that ICTs play a crucial role in connecting
people and communities, increasing innovation and productivity, improving living standards,
strengthening competitiveness, supporting economic and social modernization, and reducing
poverty worldwide.
The paper aims to examine the level of digital competitiveness of European countries by
proposing a methodology for a composite index of digital competitiveness using multi-criteria
decision-making methods in the process of aggregation data. In the primary step of the creation
of a composite index, the proposed methodology determines the relative importance of indicators
in the model using an objective statistical approach based on decision matrix data. In this
segment, the paper contributes to existing methodologies which measure digital competitiveness
by aggregation based on a linear combination, aggregation with equal criteria importance, or
subjectively determined weighting coefficients. The method of choice for objective importance
assessment of single indicators within the composite index is CRiteria Importance through
Intercriteria Correlation (CRITIC). The methodology applied to aggregate weighted data
is Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). Additionally,
the sub-objective of the analysis is to identify countries with similar digital competitiveness
and economic performances. The basic hypothesis is that the countries with better economic
performance have higher levels of digital competitiveness.
The paper is structured as follows: In the first section, the role of the digital economy for
competitiveness is presented, accompanied by methods for assessment of ICT development
impact on country economic performances. In the second section, the research methodology,
model development and the data used are described, while in the third section, the research results
and a discussion of results are offered. Concluding remarks pointing to scientific contribution
and further research directions are put forth in the last section.

2. THE IMPORTANCE OF THE DIGITAL COMPETITIVENESS

FOR THE ECONOMY
The digital economy and digital competitiveness are among the most commonly used terms
referring to the socio-economic development perspectives of contemporary society. In a broader
sense, the digital economy describes the development of a technological society and implies
the widespread use of ICTs in all spheres of human activity. ICTs enable people to perform
ordinary tasks more efficiently and have emerged as a response to societal needs (Sendlhofer
& Lernborg, 2018). In addition to the impact on individuals, ICTs also have an important
impact on companies, since they provide new opportunities for companies and facilitate the
worldwide availability of their products and services (Elia et al., 2016). ICTs have contributed to
transforming the nature and handling of the uncertainties typical for the entrepreneurial process

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 and its outcomes (Nambisan, 2017). The advantages of applying ICTs in companies are numerous
(Rossato & Castellani, 2020): improved efficiency and effectiveness of business processes,
improved understanding of user experience, increased creation and transfer of knowledge,
increased awareness of the cultural value of the company’s heritage, and the development of
state-of-the-art employee skills. The advent of the digital economy was facilitated by the digital
revolution, also known as digitalization, which represents a transition from analogue or physical
technologies to digital data systems (Dufva & Dufva, 2019).
Carlsson (2004) states that digitalization of information, combined with the Internet, creates
a wide range of various combinations of information and knowledge use through which the
application of modern technologies and the availability of greater technical possibilities can be
turned into economic possibilities. The Internet of Everything, aided by economies of scale
and platforms such as consumer electronics, mobile devices, and urban infrastructure, enable
the wide availability of services to consumers as well as easier access to potential consumers
(Leviäkangas, 2016).
The relationship between ICTs and economic growth is an issue of particular interest in terms
of both theory and practice. There are two prevailing understandings about the impact of ICTs
application on economic growth (Thompson Jr & Garbacz, 2011): direct impact, which implies
productivity improvements resulting from the application of ICTs, and indirect impact, which
means the materialization of externalities resulting from the application and development of
ICT. Several studies have reported a positive link between the development and implementation
of ICTs and economic growth (Myovella et al., (2020). Portillo et al., 2020; Vu et al., 2020;
Bahrini & Qaffas, 2019; Nair et al., 2020). Evidence indicates that ICTs improve various aspects
of productivity (Skorupinska & Torrent-Sellens, 2017; Corrado et al., 2017; Pieri et al., 2018;
Kılıçaslan et al., 2017, Ivanović-Đukić, et al., 2019; Haller & Lyons, 2019;). The digitalization and
digital economy contribute to productivity growth in many ways (Wyckoff, 2016): by creating
new innovative businesses and reducing the number of businesses with outdated, non-innovative
operations; enabling smarter, more efficient use of labour and capital to create so-called multi-
factor productivity growth through which even older firms can improve; introducing new
opportunities and services for individuals previously removed from the global economy (such
as farmers and local producers); and enhancing the efficiency of inventory management and
shipping.
Examining the impact of ICTs on economic growth is of great importance to policymakers, as
it provides them with guidance for creating development strategies. Nevertheless, it should be
borne in mind that a large number of indicators of digital development and competitiveness
exist, and that most research uses only some of these as proxies, thus all aspects of digital
competitiveness have not been covered. The following are most commonly used as proxies in
the literature: mobile and fixed broadband (Thompson Jr & Garbacz, 2011), broadband speed
(Mayer et al., 2020), fixed and mobile phone subscriptions (Albiman & Sulong, 2017), and digital
subscriber line broadband services (Haller & Lyons, 2019), investments in ICT (Niebel, 2018).
For a detailed overview of digital development proxies, see Vu et al. (2020).
Measuring and comparing countries based on digital competitiveness is a topical issue, where
several methodologies for quantification have been proposed. World Economic Forum has

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 offered the Networked Readiness Index (NRI) for measuring the propensity of a country to
take advantage of the opportunities offered by ICTs (NRI, 2019). This index measures the
performance of economies in using ICTs to boost competitiveness, innovation and well-being.
Another methodology is the Digital Economy and Society Index (DESI, 2019) developed by the
European Commission. It is a complex index that summarizes relevant indicators on European
digital performance and tracks the development of EU Member States in digital competitiveness.
In 2017 the DECA (Digital Economy Country Assessment) program was developed and tested
(Ashmarina et al., 2020). DECA is a multivariate model that involves analysing the readiness,
use and impact of digital transformation on national socio-economic progress. The DECA
methodology is focused on assessing the current level of development of the digital economy
to identify critical shortcomings, challenges and opportunities for future growth, as well as
areas that require more careful analysis. The United Nations International Telecommunication
Union published the ICT Development Index (IDI, 2018) aimed at comparing and monitoring
the development of ICT between countries and over time. E-government Development Index
(EGDI, 2021) was developed to examine the development of e-government in the member
states of the United Nations. Additionally, several authors have proposed composite indices of
digitalization and digital competitiveness (Yoo et al., 2018; Milenkovic et al., 2016; Nair et al.,
2020; Ali et al., 2020a; Ali et al., 2020b).
The construction of composite indices has specific critical steps on which the whole process
depends and which are primarily related to the determination of appropriate weighting and
aggregation methods (Saisana & Tarantola, 2002). When it comes to weighting methods when
constructing composite indices, they can be grouped into three main categories (El Gibari et
al., 2019): equal weighting, data-based methods, and participation-based methods. The equal
weighting method has the least computational complexity but has drawbacks reflected in the
loss of information (Nardo et al., 2005). The participation-based methods incorporate intuition,
the subjective system of values and knowledge of the decision-maker or group, which is also a
disadvantage of this approach because the weighting coefficients depend on their subjective
assessment and perception. The data-based methods perform criteria weights determination
based on data from the decision matrix, which eliminates the subjectivity of decision-makers,
and weight determination is performed using mathematical and statistical methods based on
information from the model. Yet, despite the apparent shortcomings, most of the stated indices
of digital competitiveness use equal weights when determining weights (Pérez-Castro et al.,
2021).
When it comes to aggregation methods, criteria can be aggregated into a composite index in
several ways: linear aggregation, geometric aggregation or multicriteria analysis. Each method
implies different assumptions and has specific consequences (Nardo, 2005). Still, it should be
noted that one of the advantages of multicriteria analysis methods is that the application of
these methods leads to the creation of composite indices that are non-compensatory or partially
compensatory.
The need to create an adequate composite measure for assessing and monitoring the digital
competitiveness of countries stems from the fact that accelerated technological development
imposes the urge to make effective strategic decisions related to the digital future, as well as

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 to assess the level of digital development and competitiveness of countries (Alam et al., 2018).
Having in mind the diversity and variety of indicators, it is desirable to create a unique composite
indicator of digital development and competitiveness that will include various aspects of
digitalization. The digital economy and digital competitiveness have a multidimensional nature
and can be defined as a multiple-criteria phenomenon (Balcerzak & Bernard, 2017). Therefore,
this paper aims to create the composite index for the measurement of digital competitiveness on
the sample of European countries using multi-criteria analysis methods.
The contribution of the paper is reflected in the creation of a new composite index of digital
competitiveness, which, unlike most existing composite indices, uses objectively determining
weighting coefficients. Namely, most of the proposed composite indices for measuring digital
competitiveness give equal importance to the indicators that make up the composite index, which
makes some indicators overestimated or underestimated. The proposed model uses an objective
approach to determining weights, which determines the weights of criteria in a multi-criteria
model based on data from the decision matrix, thus eliminating the subjectivity of decision-
makers and determining weights based on information from the model itself. To summarize,
the methodology used in this analysis makes three contributes to the construction of a complex
digital competitiveness index: (i) demonstrates the possibility of creating objective data-based
weights of criteria by which the composite index is aggregated; (ii) points to the possibility of
weights to provide adequate information to policymakers regarding the identification of priority
areas when it comes to digital competitiveness of countries; and (iii) leads to the elimination
of decision-maker subjectivity that may result in biased results. In addition, most of the above-
mentioned composite indices were created by aggregating data from diverse sources. However,
the use of data from different sources can jeopardize the correctness and reliability of the data
used, which can inadvertently affect the obtained results (Akande et al., 2019). To obtain reliable
and verifiable results, it is desirable to use data from a single, dependable database, such as
Eurostat. Therefore, only data from the Eurostat database on the digital economy and society
were used in this paper to assess the digital competitiveness of European countries.

3. RESEARCH OBJECTIVE, METHODOLOGY AND DATA

The main objective of this paper is to assess the digital competitiveness of European countries
using a two-step analysis. In the first step, the weighting coefficients of the criteria will be
obtained using CRITIC methods. In contrast, in the second step, the assessment and ranking
of countries according to the achieved level of digital competitiveness will be performed using
TOPSIS methods. Additionally, the sub-objective of the paper is to identify the groups of
European countries with similar digital competitiveness and economic performances.

3.1 CRITIC method

CRITIC (CRiteria Importance Through Intercriteria Correlation) was proposed by Diakoulaki
et al. (1995) as one of the possible ways to determine the objective values of the weighting
coefficients of criteria. The method is based on the difference and the conflict between the
criteria inherent to multi-criteria decision-making problems. The CRITIC method represents
a correlation method where the process of determining the criteria weights requires the use of
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 standard deviation of the normalized criteria values, as well as the correlation coefficients of
all pairs of criteria (Žižović et al., 2020). The CRITIC method algorithm consists of six steps
(Diakoulaki et al., 1995):
Step 1: Normalization of criteria values using the linear normalization relations depending on
the type of criteria:
r ij = (xij- xijmin)/(xijmax - xijmin) (1)
r ij = (xijmax - xij)/(xijmax - xijmin) (2)
wherein xijmax = max(i)xij and xijmin = min(i)xij , i = 1, 2,…,m, j = 1, 2,…, n.
Step 2: Determination of the standard deviation σj of each vector rj in the normalized decision
matrix.
Step 3: Construction of a symmetric matrix with elements R ij representing the correlation
coefficients between each pair of normalized criteria in the model.
Step 4: Determination of the measure of conflict between criteria:
∑nj=1 (1-R ij) (3)
Step 5: Determination of the amount of information Cj emitted by the jth criterion:
Cj = σ j ∑nj=1 (1-R ij) (4)
The larger the value of Cj, the greater is the amount of information contained in a given criterion,
and, consequently, that criterion has greater relative importance.
Step 6: Determination of the criteria weighs using the relation:
wj = Cj/( ∑nj=1Cj ) (5)

3.2 TOPSIS method

TOPSIS represents an acronym for The Technique for Order of Preference by Similarity to Ideal
Solution. It is a multi-criteria analysis method developed by Hwang & Yoon (1981). The essence of
this method is that the optimal solution should be closest to the Positive Ideal Solution (PIS) and
farthest from the Negative Ideal Solution (NIS) in a geometric sense (Chen et al., 2020). The ideal
solution is the point where the utility for the decision-maker is greatest, that is, the point where the
value of the revenue criteria is the highest. At the same time, the value of the expenditure criteria
is the lowest. The ideal solution is usually not achievable, but all multi-criteria analysis methods
tend to keep the optimal solution as close as possible to the ideal one. The main advantage of
the TOPSIS method is its low mathematical complexity and ease of use (Rajak & Shaw, 2019). In
addition, the attractiveness of the TOPSIS method is enhanced by the fact that it requires minimal
inputs from decision-makers, i.e., the only subjective data required are criteria weight (Olson, 2004).
The TOPSIS method can be represented by the following algorithm (Yoon & Hwang, 1995; Kuo,
2017):
Step 1. The beginning of the TOPSIS method algorithm requires the determination of a
normalized decision matrix with rij coefficients, whereby rij coefficients are determined using
the following relation:

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 𝑥𝑥𝑖𝑖𝑖𝑖
𝑟𝑟𝑖𝑖𝑖𝑖 = , 𝑖𝑖 = 1,2 … 𝑚𝑚, 𝑗𝑗 = 1,2 … 𝑛𝑛  (6) (6)
√∑𝑚𝑚 2
𝑖𝑖=1 𝑥𝑥𝑖𝑖𝑖𝑖

𝑣𝑣𝑖𝑖𝑖𝑖 =2.𝑟𝑟𝑖𝑖𝑖𝑖
Step In∙ this
𝑤𝑤𝑗𝑗 step, the coefficients v that form a preferentially normalized matrix are calculated. (7)
ij
The
𝐴𝐴 =calculation
∗
{𝑣𝑣1 , 𝑣𝑣2 , … , of
∗ ∗
𝑣𝑣𝑗𝑗 ,the
∗
… , 𝑣𝑣v𝑛𝑛ij ,coefficients
∗
} = {(max 𝑣𝑣is done by applying the relation:
𝑖𝑖𝑖𝑖 |𝑗𝑗 ∈ 𝐽𝐽1 ) ∧ (min 𝑣𝑣𝑖𝑖𝑖𝑖 |𝑗𝑗 ∈ 𝐽𝐽2 ), 𝑖𝑖 = 1,2, … 𝑚𝑚} (8)
𝑖𝑖 𝑖𝑖
vij −= r ij∙wj𝑥𝑥 
−𝑖𝑖𝑖𝑖 − − − (7)
𝑟𝑟𝐴𝐴𝑖𝑖𝑖𝑖 = = {𝑣𝑣1𝑥𝑥𝑖𝑖𝑖𝑖, 𝑣𝑣2 ,,… 𝑖𝑖 =, 𝑣𝑣𝑗𝑗1,2 , ……, 𝑣𝑣𝑚𝑚,𝑛𝑛 ,𝑗𝑗} = = 1,2
{(min … 𝑛𝑛𝑣𝑣𝑖𝑖𝑖𝑖 |𝑗𝑗 ∈ 𝐽𝐽1 ) ∧ (max 𝑣𝑣𝑖𝑖𝑖𝑖 |𝑗𝑗 ∈ 𝐽𝐽2 ), 𝑖𝑖 = 1,2, … 𝑚𝑚} (6)
(9)
𝑟𝑟 =
Step 3.√∑The
𝑖𝑖𝑖𝑖 √∑𝑚𝑚 𝑖𝑖=1 𝑥𝑥 2 , 𝑖𝑖 = 1,2 … 𝑚𝑚, 𝑗𝑗 = 1,2 … 𝑖𝑖 𝑛𝑛
third step of the TOPSIS method algorithm involves determining the PIS and the
𝑖𝑖 (6)
𝑚𝑚 𝑖𝑖𝑖𝑖 2
𝑖𝑖=1 𝑥𝑥𝑖𝑖𝑖𝑖
∗ 𝑛𝑛 ∗ 2
NIS.
𝑆𝑆 = The
√ ∑
𝑣𝑣𝑖𝑖𝑖𝑖𝑖𝑖 = 𝑟𝑟𝑖𝑖𝑖𝑖 ∙𝑗𝑗=1 elements
𝑤𝑤𝑗𝑗 𝑖𝑖𝑖𝑖 − 𝑣𝑣of
(𝑣𝑣 𝑗𝑗 )the ,PIS 𝑖𝑖 = v1,2j and
* … 𝑚𝑚 the NIS vj- are determined using relations: (10)
(7)
𝑣𝑣𝑖𝑖𝑖𝑖 = 𝑟𝑟𝑖𝑖𝑖𝑖 ∙ 𝑤𝑤𝑗𝑗 (7)
∗ ∗ ∗ ∗ ∗
𝐴𝐴−∗ = {𝑣𝑣1∗, 𝑛𝑛𝑣𝑣2∗, … , 𝑣𝑣𝑗𝑗∗, …−, 𝑣𝑣2𝑛𝑛∗, } = {(max 𝑣𝑣𝑖𝑖𝑖𝑖 |𝑗𝑗 ∈ 𝐽𝐽1 ) ∧ (min 𝑣𝑣𝑖𝑖𝑖𝑖 |𝑗𝑗 ∈ 𝐽𝐽2 ), 𝑖𝑖 = 1,2, … 𝑚𝑚}  (8) (8)
𝑆𝑆𝐴𝐴𝑖𝑖 = {𝑣𝑣 √∑ 1 ,𝑗𝑗=1 𝑣𝑣2 ,(𝑣𝑣 …𝑖𝑖𝑖𝑖, 𝑣𝑣−𝑗𝑗 ,𝑣𝑣…𝑗𝑗 ,)𝑣𝑣𝑛𝑛 , }, 𝑖𝑖=={(max1,2𝑖𝑖 … 𝑣𝑣𝑚𝑚𝑖𝑖𝑖𝑖 |𝑗𝑗 ∈ 𝐽𝐽1 ) ∧ (min
𝑖𝑖 𝑣𝑣 |𝑗𝑗 ∈ 𝐽𝐽 ), 𝑖𝑖 = 1,2, … 𝑚𝑚}
𝑖𝑖𝑖𝑖 2
(11)
(8)
𝑖𝑖 𝑖𝑖
− − − − −
𝐴𝐴 − = {𝑣𝑣1𝑥𝑥−𝑖𝑖𝑖𝑖, 𝑣𝑣2−, … , 𝑣𝑣𝑗𝑗−, … , 𝑣𝑣𝑛𝑛−, } = {(min 𝑣𝑣𝑖𝑖𝑖𝑖 |𝑗𝑗 ∈ 𝐽𝐽1 ) ∧ (max 𝑣𝑣𝑖𝑖𝑖𝑖 |𝑗𝑗 ∈ 𝐽𝐽2 ), 𝑖𝑖 = 1,2, … 𝑚𝑚} (9)
𝑟𝑟𝐴𝐴𝑖𝑖𝑖𝑖 =
= {𝑣𝑣1 , 𝑣𝑣2 , ,… 𝑖𝑖 =, 𝑣𝑣𝑗𝑗1,2 , ……, 𝑣𝑣𝑚𝑚,𝑛𝑛 , 𝑗𝑗} = {(min
= 1,2 𝑖𝑖 𝑛𝑛𝑣𝑣 |𝑗𝑗 ∈ 𝐽𝐽 ) ∧ (max
… 𝑖𝑖𝑖𝑖 1
𝑖𝑖 𝑣𝑣𝑖𝑖𝑖𝑖 |𝑗𝑗 ∈ 𝐽𝐽2 ), 𝑖𝑖 = 1,2, … 𝑚𝑚}  (6) (9) (9)
𝑖𝑖 𝑖𝑖
√∑𝑚𝑚 𝑖𝑖=1
2
𝑥𝑥𝑖𝑖𝑖𝑖
∗ 𝑛𝑛 ∗
𝑆𝑆𝑖𝑖∗ = √∑𝑗𝑗=1 2
(𝑣𝑣𝑖𝑖𝑖𝑖 − 𝑣𝑣𝑗𝑗∗) , 𝑖𝑖 = 1,2 … 𝑚𝑚 (10)
𝑆𝑆
𝑣𝑣𝑖𝑖𝑖𝑖𝑖𝑖 = ∑∙𝑛𝑛𝑗𝑗=1 𝑣𝑣𝑗𝑗 )2 , 𝑖𝑖 = 1,2 … 𝑚𝑚 (10)
where =√ 𝑟𝑟J𝑖𝑖𝑖𝑖
1 𝑥𝑥 a 𝑗𝑗(𝑣𝑣
is𝑖𝑖𝑖𝑖𝑤𝑤 set𝑖𝑖𝑖𝑖of−revenue criteria and J2 is a set of expenditure criteria. (7)
𝑟𝑟𝑖𝑖𝑖𝑖− = 𝑛𝑛 ∗2
, 𝑖𝑖 = 1,2 − 2∗
… 𝑚𝑚, 𝑗𝑗 = 1,2 … 𝑛𝑛 (6)
𝑆𝑆𝐴𝐴𝑖𝑖− =
Step =4.√√ ∑
{𝑣𝑣∑The
𝑚𝑚
,(𝑣𝑣
𝑛𝑛𝑣𝑣𝑥𝑥2main …𝑖𝑖𝑖𝑖, 𝑣𝑣− ∗
step𝑣𝑣𝑗𝑗−,)of , }, 𝑖𝑖=TOPSIS
𝑣𝑣2𝑛𝑛the ={(max
1,2 … 𝑣𝑣𝑚𝑚 method (11)
𝑖𝑖𝑖𝑖 |𝑗𝑗 ∈ 𝐽𝐽1involves
) ∧ (mindetermining
𝑣𝑣𝑖𝑖𝑖𝑖 |𝑗𝑗 ∈ 𝐽𝐽2 ), 𝑖𝑖 the distance
… 𝑚𝑚} of an alternative
∗ ∗
,𝑗𝑗=1
1𝑖𝑖=1 𝑖𝑖𝑖𝑖 𝑗𝑗 , … = 1,2, (8)
𝑆𝑆𝑖𝑖 = √∑ 𝑗𝑗=1(𝑣𝑣𝑖𝑖𝑖𝑖 − 𝑣𝑣𝑗𝑗 ) , 𝑖𝑖 = 1,2𝑖𝑖 … 𝑚𝑚 𝑖𝑖 (11)
from
𝑣𝑣𝑖𝑖𝑖𝑖− =the
𝑟𝑟𝑖𝑖𝑖𝑖 −∙PIS
𝑤𝑤𝑗𝑗−and the NIS. The relation for determining the distance between the alternative (7) and
𝐴𝐴 PIS
the = {𝑣𝑣 is1 ,given , 𝑣𝑣𝑗𝑗− , … , 𝑣𝑣𝑛𝑛− , } = {(min 𝑣𝑣𝑖𝑖𝑖𝑖 |𝑗𝑗 ∈ 𝐽𝐽1 ) ∧ (max 𝑣𝑣𝑖𝑖𝑖𝑖 |𝑗𝑗 ∈ 𝐽𝐽2 ), 𝑖𝑖 = 1,2, … 𝑚𝑚}
𝑣𝑣2 , …by: (9)
𝑖𝑖 𝑖𝑖
𝐴𝐴∗ = {𝑣𝑣1∗ , 𝑣𝑣2∗ , … , 𝑣𝑣𝑗𝑗∗ , … , 𝑣𝑣𝑛𝑛∗ , } = {(max 𝑣𝑣𝑖𝑖𝑖𝑖 |𝑗𝑗 ∈ 𝐽𝐽1 ) ∧ (min 𝑣𝑣𝑖𝑖𝑖𝑖 |𝑗𝑗 ∈ 𝐽𝐽2 ), 𝑖𝑖 = 1,2, … 𝑚𝑚} (8)
𝑆𝑆𝑖𝑖∗ = √∑𝑛𝑛𝑗𝑗=1(𝑣𝑣𝑖𝑖𝑖𝑖 − 𝑣𝑣𝑗𝑗∗ )2 , 𝑖𝑖 = 1,2𝑖𝑖… 𝑚𝑚  (10) (10)
𝑖𝑖
− − − − −
𝐴𝐴 = {𝑣𝑣1 , 𝑣𝑣2 , … , 𝑣𝑣𝑗𝑗 , … , 𝑣𝑣𝑛𝑛 , } = {(min 𝑣𝑣𝑖𝑖𝑖𝑖 |𝑗𝑗 ∈ 𝐽𝐽1 ) ∧ (max 𝑣𝑣𝑖𝑖𝑖𝑖 |𝑗𝑗 ∈ 𝐽𝐽2 ), 𝑖𝑖 = 1,2, … 𝑚𝑚} (9)
𝑖𝑖 𝑖𝑖
− 𝑛𝑛 − 2
𝑆𝑆𝑖𝑖 =
On the√∑ 𝑗𝑗=1(𝑣𝑣hand,
other 𝑖𝑖𝑖𝑖 − 𝑣𝑣𝑗𝑗the
) relation
, 𝑖𝑖 = 1,2
for… determining
𝑚𝑚 the distance between the alternative(11)
and the
𝑆𝑆𝑖𝑖∗ = √∑𝑛𝑛𝑗𝑗=1(𝑣𝑣𝑖𝑖𝑖𝑖 − 𝑣𝑣𝑗𝑗∗ )2 , 𝑖𝑖 = 1,2 … 𝑚𝑚 (10)
NIS is given by:

𝑆𝑆𝑖𝑖− = √∑𝑛𝑛𝑗𝑗=1(𝑣𝑣𝑖𝑖𝑖𝑖 − 𝑣𝑣𝑗𝑗− )2 , 𝑖𝑖 = 1,2 … 𝑚𝑚  (11) (11)

Step 5. In this step, the approximation index (Ci*) is determined, that is, the relative proximity of
the considered alternative to the PIS according to the relation:
Ci* = (Si-)/(Si*+Si- ), i = 1,2,….m  (12)
Step 6. In the final step of the TOPSIS method, alternatives are ranked based on the approximation
index in descending order to obtain the best alternative.

3.3. Cluster analysis

Cluster analysis represents one of the most proficient methods for data processing used to
identify homogeneous sets within a heterogeneous group (Fox et al., 1991). It is an approach
used to detect complex relationships between variables. Cluster analysis involves grouping a set
of objects in a way that the objects in one group are similar to each other, and at the same time,
differ from objects in other groups (Esmalifalak et al., 2015). The ease of use of cluster analysis
is the reason for the popularity of this approach. Variables applied in the cluster analysis have the
same importance (there are no dependant and independent variables) since the purpose of cluster
analysis is to recognize patterns among variables rather than predicting a particular value. Each
object in the cluster analysis represents a separate point in multidimensional space defined by the
values of its attributes, where the similarity between the two objects is determined based on their

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 distance (Zeng et al., 2008). The clustering process aims to identify similarities in the variable
structure and create homogeneous groups of objects based on the identified similarities. Several
cluster procedures can be identified, whereby an agglomerative hierarchical cluster analysis will
be applied in this paper. The essence of this approach is that it starts with each of the n objects
being a cluster, with similar objects being merged in each subsequent step until each of the
objects is deployed into relatively homogeneous groups. Therefore, the agglomerative clustering
strategy is considered a bottom-up strategy since each object represents a separate cluster at the
beginning. Then the cluster pairs merge as the hierarchy increases (Chakraborty et al., 2020).
The first step in the cluster analysis is the determination of the distance between objects. There
are various methods for determining the distance between objects (such as Euclidean distance,
squared Euclidean distance, Manhattan distance, Maximum distance, Mahalanobis distance),
whereby squared Euclidean distance will be used in this paper. In the next step, the grouping
of objects is performed. There are various agglomeration methods (Olson, 1995), whereby in
the paper, Ward’s procedure will be applied. The essence of this method is not to calculate
the distance between the clusters but to maximize the homogeneity within the cluster. Ward’s
method has several advantages (Ünal & Shao, 2019): it allows maximizing homogeneity within
the cluster, allows minimizing cluster heterogeneity, and leads to the robustness of results. The
outcomes of hierarchical clustering are usually represented in the form of a dendrogram which
illustrates the clusters as the nodes of a tree-like data structure (Chakraborty et al., 2020).

3.4 Data and model development

Digital competitiveness is estimated for a sample of 30 European countries based on data
regarding the digital economy and society obtained from the Eurostat Digital Economy and
Society database for the year 2019 (Eurostat, 2020a). As data on the ICT sector were not available
for all countries, nor for 2019, indicators related to the ICT sector were not taken into account in
the analysis, as the sample size would be significantly reduced. Therefore, the indicators used to
assess digital competitiveness include 13 indicators grouped into three categories.
The first category, named ICT usage in households and by individuals, encompasses indicators
such as the percentage of individuals that has used the Internet in the last three months (Internet
use), the percentage of households with Internet access (Connection to the Internet and computer
use), the percentage of individuals that has used the Internet to obtain the services of public
institutions or administrative entities within last 12 months (E-government), the percentage
of individuals that used the Internet to purchase products or services in the last three months
(E-commerce) and the percentage of individuals that has used computers, laptops, smartphones,
tablets or other portable devices at work (ICT usage at work). The second category referred to
as ICT usage in enterprises includes indicators related to the percentage of enterprises that have
a website (Website and use of social media), the percentage of enterprises with ERP software
package to share information between different functional areas (E-business), the percentage of
enterprises with e-commerce sale (E-commerce), the percentage of employees using computers
with Internet access compared to the total number of employees (Connection to the Internet),
the percentage of enterprises that have Internet access relative to the total number of enterprises
in the country, and the percentage of enterprises that use strong password authentication as an

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 ICT security measure (ICT security). The third category, named digital skills, involves indicators
such as the percentage of the population with low digital skills (ICT users), the percentage of
employed ICT specialists as a share of total employment (ICT specialists in employment), and
the percentage of enterprises that have provided training for employees to develop or improve
digital skills (ICT training).
Categories represent criteria in the model, while the indicators represent sub-criteria (Figure 1).

Ranking European countries according to digital

competitiveness composite index

C1. ICT usage in C2. ICT usage in

C3. Digital skills
households and by enterprises
individuals

C1.1. Internet use C2.1. Websites and use of social media

C1.2. Connection to the Internet C2.2. E-business C3.1. ICT users
and computer use C2.3. E-commerce (enterprises with e- C3.2. ICT specialists in
C1.3. E-government commerce sales) employment
C1.4. E-commerce (online C2.4. Connection to the Internet C3.3. ICT training
purchase in last three months) (enterprises with internet access)
C1.5. ICT usage at work C2.5. ICT security (security measure used)

EU 28, Norway, Serbia

Fig. 1 – Hierarchical structure of the model. Source: own research

Fig. 1 – Hierarchical structure of the model. Source: own research

4. RESULTS AND DISCUSSION

Using the CRITIC methods, the following weights of criteria and sub-criteria were determined
(Table 1):
Tab. 1 – Relative significance of criteria and sub-criteria. Source: own research
Criteria Sub-criteria Sub-criteria weights Criteria weights
Internet use 0.062287121
ICT Connection to the Internet and
0.080473564
usage in computer use
households E-government 0.057049018 0.3201145
and by E-commerce (online purchase in the
0.065249476
individuals last three months)
ICT usage at work 0.055055319

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 Websites and use of social media 0.064372506
E-business 0.111007268
ICT E-commerce (enterprises with
0.092766335
usage in e-commerce sales) 0.46628311
enterprises Connection to the Internet
0.077118394
(enterprises with internet access)
ICT security (security measure used) 0.121018607
ICT users 0.080599667
Digital
ICT specialists in employment 0.062194305 0.21360239
skills
ICT training 0.070808420

Based on the obtained results, it can be noted that the category ICT usage in the enterprises has the
highest relative importance in assessing the achieved level of digital competitiveness. Regarding
sub-criteria, the most important sub-criteria in assessing countries’ digital competitiveness is
related to ICT security and E-business. This means that the digital performance of the country
is most significantly affected by the level of development of the ICT sector in enterprises. In
contrast, the usage of ICT in households is not crucial. Also, the level of digital skills is less
important than the importance of ICT usage in enterprises. Additionally, when looking at the
sub-criteria within the criteria of ICT usage in enterprises, it can be noticed that the criteria
related to the commercial use of ICT (such as e-commerce) are less important than the criteria
related to non-commercial use of ICT (such as online security), which is following the results
obtained by Milošević et al. (2018).
In the second part of the analysis, the TOPSIS method was applied to evaluate and rank countries
based on their digital competitiveness. The results are shown in Table 2.

Tab. 2 – Country rankings according to the level of achieved digital competitiveness. Source:
own research
Country Digital com- Rank Country Digital com- Rank
petitiveness petitiveness
index index

Finland 0.762145886 1 Slovenia 0.484931879 16

Netherlands 0.740252087 2 Spain 0.479319590 17
Denmark 0.737697856 3 Estonia 0.476872351 18
Sweden 0.704007455 4 Portugal 0.449256463 19
Norway 0.696300532 5 Serbia 0.437748381 20
Belgium 0.664988713 6 Slovakia 0.392472813 21
United Kingdom 0.596010034 7 Cyprus 0.380482019 22
Ireland 0.576294361 8 Latvia 0.372013043 23
Austria 0.569541227 9 Croatia 0.358278487 24

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 Germany 0.565424996 10 Italy 0.357726914 25
Czech Republic 0.563265193 11 Poland 0.322461394 26
Luxembourg 0.539312451 12 Greece 0.318395071 27
France 0.525897882 13 Hungary 0.260716169 28
Malta 0.520077868 14 Bulgaria 0.167774072 29
Lithuania 0.487942569 15 Romania 0.105482473 30

The results indicate that Nordic countries achieve the highest values of digital competitiveness,
while most of the Eastern European countries are at the bottom of the list. If the obtained
results are compared with similar indices measuring the level of digital development, such as
the Network Readiness Index (NRI, 2019), ICT Development Index (IDI, 2018), IMD World
Digital Competitiveness Ranking (IMD, 2019), and Digital Economy and Society Index (DESI,
2019), similarities can be seen both in the countries at the top of the list and in the countries
at the bottom of the list. According to DESI (2019), Finland, Sweden, Denmark and the
Netherlands scored the highest. Similarly, the results of NRI (2019) indicate that eight European
nations rank among the top ten countries in the world: Sweden (1), the Netherlands (3), Norway
(4), Switzerland (5), Denmark (6), Finland (7), Germany (9), and the United Kingdom (10). In
addition, Nordic countries, the Netherlands and Switzerland can be found among the highest-
ranked countries in the IMD World Digital Competitiveness Ranking. The similarities in ranking
indicate the validity of the proposed methodology.
The results of the correlation analysis indicate that the application of equal weights leads to
moderate rank reversal (the value of Kendall’s tau is 0.903). Therefore, whenever possible it is
desirable to apply objective methods of weight determination. Regarding the sensitivity of the
results, although there is a rank reversal, it is not intensely expressed, a finding which supports
the robustness of the results.
To determine groups of countries with similar digital competitiveness and economic performances,
a cluster analysis was performed, for which the first and the most important step is the selection
of the variables. Besides the assessed digital competitiveness, three more variables were used in the
analysis which reflects the economic performance of analysed countries (Table 3).

Tab. 3 – Variables for cluster analysis. Source: own research

Variable Description Source

Assessed value based on the data related to the

Digital competitive-
digital economy and society using integrated Own research
ness
CRITIC-TOPSIS method
Output per worker (GDP constant 2011 inter-
Labour productivity ILOSTAT (2020)
national $ in PPP)
Share of employed persons aged 20 to 64 in the
Employment rate Eurostat (2020b)
total population of the same age group

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 The median of the total income of all house-
holds after tax and other deductions that is
Median equalised net
available for spending or saving, divided by the Eurostat (2020c)
income
number of household members converted into
equivalised adults
After selecting appropriate variables, a cluster analysis was applied and four distinct groups of
countries were identified (Table 4).

Tab. 4 – Composition of clusters. Source: own research

Cluster 1 Cyprus, Czech Republic, Estonia, Latvia, Lithuania, Malta, Portugal, Slovakia,
Slovenia, Spain
Cluster 2 Bulgaria, Greece, Croatia, Italy, Hungary, Poland, Romania, Serbia
Cluster 3 Denmark, Germany, Netherlands, Austria, Sweden, United Kingdom, Norway
Cluster 4 Belgium, Finland, France, Ireland, Luxembourg
Cluster 1 is the largest one, including one-third of the countries, while Cluster 4 is the smallest
with five countries. The clusters obtained include a set of geographically heterogeneous countries.
Cluster 1 has the highest diversity, consisting of countries primarily from Central and Southern
Europe and Baltic countries. Cluster 2 encompasses Balkan countries and some of the Central
European countries. Cluster 3 includes Northern and most Western European countries, while
Cluster 4 includes Western and Northern European countries.
If the data are analysed by clusters, it can be noticed that high digital competitiveness
is accompanied by better economic performance and vice versa (Table 5). Hence, there is a
link between the level of digital competitiveness and a country’s economic performance. The
difference in the global competitiveness of countries and their economic performance largely
depends on the availability, level of acceptance, and use of ICT (Mitrović, 2020). Regarding
digital competitiveness and economic performance of the clusters, Cluster 2 has the lowest
average value of digital competitiveness and also indicates the existence of considerable economic
deprivation, signifying that a lower level of digital competitiveness is associated with lower
economic performance. Regarding the countries in Cluster 1, they have higher average values
of all variables than the countries in Cluster 2. Countries in the fourth cluster have a relatively
high value of digital competitiveness and the highest values of GDP per capita and labour
productivity. In contrast, countries in Cluster 3 have the highest values of digital competitiveness
and the highest employment rates. Considering Clusters 3 and 4, it can be concluded that higher
digital competitiveness is associated with better economic performance.

Tab. 5 – Mean value of variables within clusters. Source: own research

Variable Cluster 1 Cluster 2 Cluster 3 Cluster 4
Digital competitiveness 0.4607 0.2911 0.6585 0.6137
Labour productivity 69,711.00 61,755.75 98,138.29 129,323.60
Employment rate 76.01 69.21 79.37 72.86
Median equalized net income 11,600.00 7,130.00 27,094.00 26,793.00

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 5. CONCLUSION
The overall development of the information society should be directed towards harnessing the
potential of ICTs to increase efficiency, economic growth, and higher employment to improve
the quality of life of all citizens of the countries. Digital transformation is an opportunity for
European countries to address a number of their structural economic, political and social
challenges. In recent decades, the importance of digitalization has become the subject of
numerous researches, as digitalization has changed the lives of groups and individuals in many
ways. Nevertheless, when it comes to measuring digitalization and digital competitiveness of
countries, no consensus has emerged regarding a composite indicator that would cover all
aspects of digitalization.
This paper has proposed a multi-criteria approach to create a composite measure of digital
competitiveness. Nordic countries were shown to achieve the highest degree of digital
competitiveness, while countries in Eastern Europe lag behind. Furthermore, the results
indicate that ICT usage in the enterprises has the highest relative importance with regard to
the assessment of the achieved level of digital competitiveness, which indicates that the digital
performance of a country is most significantly affected by the level of development of the ICT
sector in enterprises. In contrast, the usage of ICT in households is not crucial. Also, the level of
digital skills is less important than the importance of ICT usage in enterprises. Additionally, the
criteria related to the commercial use of ICT (such as e-commerce) are less important than the
criteria related to non-commercial use of ICT (such as online security).
Regarding the identification of groups with similar digital competitiveness and economic
performances, four distinct geographically dispersed groups of countries were identified:
countries primarily from Central and Southern Europe and Baltic countries, Balkan countries
along with some Central European countries, Northern and most Western European countries,
while the smallest fourth group includes one Western and one Northern European country. The
results indicate that groups with a low average value of digital competitiveness also have lower
economic performance, while economically advanced countries can be found in the groups of
countries with high digital competitiveness.
These results contribute to existing research on how to measure the digital economy by offering an
empirical example of assessing the digital competitiveness of European countries. Furthermore,
the results may have implications for policymakers as well as serve as a guideline for making
strategic decisions aimed at planning the digital future of the country.
Nevertheless, the proposed study has some limitations. Due to the unavailability of data,
the research does not take into account the supply side of digitalization related to regulatory
frameworks nor the countries’ investments in ICTs. Future studies will be aimed at eliminating
these shortcomings and including these variables, as they represent valuable indicators of digital
competitiveness.
Acknowledgments: The research in this paper was conducted within the framework of activities
on the bilateral cooperation project “Researching capacity for the implementation of smart cities
as the basis for sustainable urban development” financed by Ministry of Education, Science
and Technological Development of the Republic of Serbia as well as the Ministry of Science

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 and Education of the Republic of Croatia. It has also been supported by the University of
Rijeka under the project “Smart cities in function of development of national economy” (uniri-
drustv-18-255-1424).

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Contact information
Assoc. Prof. Jelena J. Stankovic, Ph.D.
University of Niš
Faculty of Economics
Serbia
E-mail: jelena.stankovic@eknfak.ni.ac.rs
ORCID: 0000-0002-9875-9861

TA Ivana Marjanovic, M.Sc.

University of Niš
Faculty of Economics
Serbia
E-mail: ivana.veselinovic@eknfak.ni.ac.rs
ORCID: 0000-0002-9526-0467

Assoc. Prof. Sasa Drez gic, Ph.D.

University of Rijeka
Faculty of Economics
Croatia
E-mail: sasa.drez gic@efri.hr
ORCID: 0000-0002-7712-8112

Prof. Zarko Popovic, Ph.D.

University of Niš
Faculty of Economics
Serbia
E-mail: zarko.popovic@eknfak.ni.ac.rs
ORCID: 0000-0002-4347-6960

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