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Organizacija, Volume 53 Issue 1, February 2020Research Papers
Drivers of Global Competitiveness in the 
European Union Countries in 2014 and 
2017
DOI: 10.2478/orga-2020-0003
Milja MARČETA1, Štefan BOJNEC2
1 Ministry of Education, Science and Sport, Masarykova 16, SI-1000 Ljubljana, Slovenia 
2 University of Primorska, Faculty of Management, Cankarjeva 5, SI-6001 Koper, Slovenia, 
stefan.bojnec@fm-kp.si;  stefan.bojnec@siol.net (corresponding author)
Background and Purpose: The main purpose of this study is to find the key drivers of Global Competitiveness In-
dex (GCI) in the European Union (EU-28) countries from the aspect of country’s global competitiveness: institutions, 
macroeconomic environment, infrastructure, higher education, market effectiveness, market size, technological 
readiness, innovation and business sophistication.
Methodology: This paper investigates global competitiveness of the EU-28 countries with the use of GCI in the 
periods 2014-2015 and 2017-2018. The correlation analysis and regression analysis are applied for testing the set 
two hypotheses. 
Results: The empirical results confirmed our hypotheses that GCI is particularly significantly positively correlated 
with innovation and business sophistication, and universities-industry collaboration in researches, and clusters de-
velopment. 
Conclusion: The paper contributes to the literature of global competitiveness, by examining the relationship of 
sub-indexes of competitiveness of the EU-28 countries, pointing out the influence of universities-industries collabo-
ration in researches and cluster development with geographic concentration of companies. The results and findings 
can be relevant for science, economic and research policy, and managerial practices that enhance innovation and 
business sophistication for research in collaboration of companies, universities, higher education institutions, and 
decision makers. The implications of this study can be important for better understanding of drivers of the EU-28 
countries global competitiveness. 
Keywords: competitiveness, economic activities, global competitiveness index, innovation
1 
Received: September 29, 2019; revised: December 30, 2019; accepted: January 17, 2020
1 Introduction
Global competitiveness of countries is a set of institution, 
policies and factors, which determine level of country pro-
ductivity (World Economic Forum – WEF, 2009). This 
means that for global competitive economic system is 
important quality of institution, which has to be ensured 
by national instruments, and namely legal infrastructure, 
laws, regulations, legal titles and stable monetary policy.
Strategic target of the Lisbon strategy for the Europe-
an Union (EU) was that Europe would become the most 
competitive, dynamic and on knowledge based economy 
in the world, with more and better work places, bigger 
social cohesion and considering environment (European 
Commission, 2010, 2).
Europe has to be capable to compete with numerous 
emerging economies in the global market such as BRIC 
(Brasil, Russia, India, and China) countries. The impor-
tance of competitiveness is significantly increasing, not 
only for the companies, but for countries and nations. Glo-
balisation usually means absorbing and expansion all over 
the world. The international trade was expanded by 73% 
from the year 1999 to the year 2009 (European Commis-
sion, 2010). This can be a reason why there is countries 
38
Organizacija, Volume 53 Issue 1, February 2020Research Papers
interest to attract inflow of capital that they can acceler-
ate economic development, to raise living standards or to 
gain and retain growth of gross domestic product (GDP). 
Different institutions have developed different methods to 
measure competitiveness.
The purpose of this paper is to analyse Global Compet-
itiveness Index (GCI), to find out its main drivers and to 
analyse the EU-28 countries from the global competitive-
ness aspect. Focus is on factors of GCI according to WEF 
(2014, 2017) for the EU-28 countries: institutions and 
institutional environment, macroeconomic environment, 
development of infrastructure, higher education, market 
effectiveness, market size, technological readiness, inno-
vation and business sophistication. Due to heterogeneity 
of the EU-28 countries, there are different phases in their 
developments and factors that influence on global compet-
itiveness achievement. For this reason, we include GDP 
per capita to find out the key drivers of global competitive-
ness. The main idea is to check correlation between GCI 
and level of economic development measured by GDP per 
capita, and other selected elements, which construct GCI. 
These evidences can be important for government compet-
itiveness policy.
Therefore, this paper aims to analyse relationship 
between indicators of sub-index and GCI, e.g. business 
sophistication and innovation, and influence of collabo-
ration between companies and universities-industries in 
researches, and clusters development with geographical 
concentration of companies in the EU-28 countries. We 
aim to answer on the following three research questions:
• Is there a significant correlation between three pillar 
groups of sub-index variables and GCI?
• What is the relationship between three pillar groups 
of sub-index variables of GCI and competitiveness? 
• What is the influence of collaboration between uni-
versities-industries in researches and clusters devel-
opment on competitiveness in the EU-28 countries? 
The rest of the paper is organized as follows: in the 
next section are presented theoretical basis and review of 
the literature. The following sections present description 
of variables included in the empirical analysis and the em-
pirical results of the statistical analysis of indicators with 
the discussion of the results. Final section concludes and 
derives main findings with policy and practical implica-
tions. 
2 Literature review and hypotheses 
development
Smith (1776) studied competitiveness and developed the-
ory of absolute comparative advantages based at low cost 
production. Ricardo (1962) developed concept of relative 
comparative advantages in international trade or com-
parative advantages that come from differences in labour 
productivity. New competitiveness theories, neoclassical 
theories and new factor endowments theories were de-
veloped. Modern theory Heckscher-Ohlin-Samuelson and 
competitive advantage of country is definite with endow-
ments of production factors, instead of production costs 
(Sheppard, 2011). Among new theories is new economic 
geography (Krugman, 1998).
Krugman (1994) on one side contradicts national com-
petitiveness as “dangerous obsession” out of a reason, that 
causes unsuitable arrangement of sources and leads to 
protectionism or trade war. On the other side, connecting 
economy on world scale potentially strengthens agglom-
eration of economy and specialization (Krugman, 1998).
Satsysk (2015) shows that modern university can be 
globally competitive in the case when it is provided with 
opportunities for engaging its talented researchers, teach-
ers and students with sufficient quantity and quality of ma-
terial/financial resources, infrastructural base and with ef-
fective governing/management model. In terms of limited 
resources, institutional and financial government support 
are aiming at modernization of university.
In the literature we can encounter different definitions 
of territorial, national and regional competitiveness. Based 
on OECD (Garelli, 2002), competitiveness of nations is a 
stage, in which country can in circumstances of free and 
open market produce goods and services, which fulfil in-
ternational standards on market, preserve and at the same 
time expand income of their own population for a long 
term. Reaching competitiveness is important at country 
level.
Porter (1998) argued that competitive advantages, 
geographical integration of industry or industrial clusters 
are vitally important. Porter (1990) emphasized indirect 
effects that can play geographical agglomeration of clus-
ters in particular field for strengthening of competitive ad-
vantages. Clusters and geographical concentration among 
interactive companies, specialized suppliers, service pro-
viders and with them related industries and institutions can 
play important role on an individual area, which can com-
pete, but can also cooperate (Porter, 2000).
Innovation can be one of the most important determi-
nants of competitiveness (Kovačič, 2007; Shamout, 2019). 
Smaller countries can through clusters and updated strat-
egies achieve that their relatively smaller country size be-
comes advantage (Pitelis, 2008). In this regards dynamic 
industrial policy of cluster developments is important in 
the EU-28 countries.
Reduction of unemployment can be linked to foster-
ing small and medium enterprises development, changes 
on labour market, the educational system and the entrepre-
neurship activities (Gričar et al., 2019; Južnik Rotar et al., 
2019). These factors became more important for competi-
tiveness in recent years. Nekrep, Boršič and Strašek (2018) 
indicated the link between expenditures for research and 
development (R&D expressed in % GDP) and labour pro-
39
Organizacija, Volume 53 Issue 1, February 2020Research Papers
ductivity based on observed data for the EU member states 
in the period 1995-2013.
Liberalization of the economy can be important factor 
for international competitiveness (Fagerberg, 1988), new 
technologies, and innovations (Fagerberg et al., 2007). The 
process of trade liberalization can rise possibilities for ex-
panding import and export for similar products, and thus 
encourage growth inside branch trade (Bojnec & Novak, 
2005).
Cross-sectional innovation platform can create a sym-
biosis between the university, the economy, and local com-
munities that manage innovation activities and technolo-
gies to increase competitiveness (Gjelsvik, 2018).
A special attention is to consider quality of institution-
al environment and particularly the role of rules and legal 
infrastructure that can effect on a business location, such 
as elimination of limitations on setting-up firms and shops, 
factors mobility, and attraction of foreign direct investment 
(FDI) (Bojnec & Fertő, 2017; 2018). In addition, the per-
centage of population of a certain age with finished tertiary 
education can be important for global competitiveness. 
The number of bachelors from the tertiary education in the 
EU countries has increased. Tertiary education expansion 
has had positive effects for incomes and wellbeing of in-
dividuals and for growth of economies (Čepar & Bojnec, 
2008, 2010; Čepar, 2009, 27). Knowledge-based econo-
mies in rapidly changing markets require organisation and 
strategies to effectively use knowledge and skills (Kareem 
& Mijbas, 2019). Many countries have increased their na-
tional competitiveness such as Israel, the Netherlands, Fin-
land, and Germany, driven by education and skilled labour 
contributing to high level of productivity and investments 
in R&D, which further promotes innovative world-class 
clusters development (Paraušić et al., 2014).
Camagni (2008) argues that institutions, rules and 
norms create conditions for reduction of market transac-
tion costs. They can provide warranty for contracts and ob-
ligations enforcements, and can help to resolve company’s 
problems related to conflicts of interests and monopoly 
power. They can create favourable business climate that is 
beneficial for local companies and can improve attractive-
ness for external companies and investors. Petryle (2016) 
examined the relationship between the GCI and GDP 
growth of countries during the period 2006-2015. It was 
found that there is a weak or no relationship in the EU-27 
countries plus Norway, Switzerland, Iceland, the United 
States, and the Russian Federation.
The WEF methodology is the most known system of 
a country’s competitiveness assessment. The datasets are 
gathered from survey information comparing development 
and competitiveness between countries in the following 
areas: institutions, infrastructure, macro environment, 
health care and education, higher education, effectiveness 
of market, size of market, technological condition, inno-
vation and business sophistication. Since 2005 the WEF 
analysis of competitiveness are based on the GCI as a 
tool, which measures microeconomic and macroeconom-
ic foundations of competitiveness of the country (WEF, 
2014, 4–5). The GCI is calculated as a cogent average of 
different components (factors), which measure specific as-
pects of competitiveness.
The GCI has passed a whole picture of territorial com-
petitiveness by countries. Therefore, the GCI is aggregated 
umbrella index, which is composed from three sub-index-
es of competitiveness development phases: (I) basic com-
petitiveness requirements (factor driven), (II) efficiency 
enhancers (efficiency-driven), and (III) innovations and 
business sophistication (innovation-driven).
If it is an efficiency-driven country, then GDP, inflation 
rate, trade, labour productivity, and costs are important de-
terminants of competitiveness, while for innovation-driv-
en country the determinants of competitiveness are GDP, 
inflation, tax rate, FDI, trade, and cost (Rusu & Roman, 
2018).
WEF defines different countries groups that are ar-
ranged based on the level of economic development meas-
ured by GDP per capita: low, middle, and high income 
countries. In addition, as an important criterion referred 
to amount of mineral resources exported in entire export 
considering development phases in a way of competition 
and country categories in the phase of transition.
2.1 Hypotheses
Following previous literature and in accordance with 
aims of our study, we set the following two hypotheses 
(H):
H1: Relationship exists between the level of the EU-
28 countries competitiveness and indicators of innovation 
and business sophistication, but weak correlation exists 
between the EU-28 countries competitiveness and sub-in-
dexes of other two pillar groups (basic conditions and effi-
ciency enhancers).
H2: University-industry collaboration in researches 
and clusters development with geographical concentration 
of companies have statistical distinctive influence on na-
tional competitiveness in the EU-28 countries. 
Empirical studies (Dima et al., 2018) have indicated 
that the highest correlation is between the GCI and R&D 
expenditure as a % of GDP (0.8257), a result that indicates 
a very strong positive relationship between innovation and 
competitiveness. 
1 
1 Some companies have an ‘employee first’ policy, with a basic premise that contented or happy employees perform better. South 
West Airlines is a well-known example. In such companies, serious demands are made on employees and strict selection pro-
cedures are in place, and teams are responsible for performance. It is far from a free-floating culture.
40
Organizacija, Volume 53 Issue 1, February 2020Research Papers
Paraušić et al. (2014) argues that the coefficient of a 
simple linear correlation indicates that there is a strong 
positive correlation in the sample between state cluster de-
velopment in a country and its national competitiveness.
While studies indicated some similarities and differ-
ences in results, in general there is expected positive im-
pacts of the analysed variables on global competitiveness, 
including for the EU-28 countries.
3 Data and Methodology
The collected publicly available secondary data are used in 
the empirical analyses. The source of data is WEF (2014, 
2017) and data from Eurostat (2014 and 2017) for GDP 
per capita for the EU-28 countries. Due to the differences 
in the level of economic development between the EU-28 
countries GDP per capita is included to check correlation 
between GCI and the level of economic development 
measured by GDP per capita, and other selected elements, 
as an additional control indicator in the analysis. For glob-
al country’s competitiveness evaluation, the GCI is used. 
The GCI is constructed from indicators, which are evalu-
ated based on scale from 1 to 7. We used 40 variables. The 
applied methodology was empirical the analysis of indica-
tors: institutions, macroeconomic environment, develop-
ment of infrastructure, higher education, market effective-
ness, market size, technological readiness, innovation, and 
business sophistication. We used correlation and regres-
sion analyses to test the hypotheses. The Pearson’s corre-
lation coefficient is applied to investigate the relationship 
between GCI and indicators of country’s global compet-
itiveness. Furthermore, the regression analysis is applied 
for validation of tested models and their assessed fittings. 
The IBM SPSS software was used for the data analyses. 
4 Drivers of global competitiveness 
4.1 Basic requirements and global 
competitiveness
Indicators of basic requirements are referred to quality of 
institutions or institutional management, macroeconomic 
environment, infrastructure, health care, and basic edu-
cation (WEF, 2014). In addition to basic competitiveness 
requirements, there are included the following drivers of 
global competitiveness (Table 1): 
Table 1 presents the analysed basic requirement var-
iables and their expected a positive or a negative sign of 
correlation coefficient with GCI.
Table 1: Expected sign of correlation between GCI and basic requirements
Variables of basic requirements                                                                Sign of correlation
Property rights and intellectual property – owners are not willing to invest their company 
shares in improvement and maintenance of their assets, if their ownerships on intellectual 
property rights are not protected, propert, intel. prop.
+
Corruption – means dishonesty at treatment of public orders, lack of visibility and reliability, 
inability to assure suitable services for business sector and political dependence of judiciary, 
what causes substantial economic costs to companies and slowdown process of economic 
development, corruption
+
Government regulation – government attitude to market and freedom is very important, as 
exaggerated bureaucracy can negatively effect on operation effectiveness, gov. reg.
+
Legal framework in setting disputes, legal and judicial system for company in which individu-
al, companies and governments communicate, because they are important for creating wealth, 
leg. dis. 
+
Quality of roads, ports, airports, qual road, qual port, qualia airport +
Source: WEF (2014, 2017) Country economy profiles, 99-104.
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Organizacija, Volume 53 Issue 1, February 2020Research Papers
Fixed telephone lines and per 100 inhabitants – the number of working landline telephone, 
telfix
+
Mobile telephone per 100 inhabitants – the number of subscription to a public mobile tele-
phone service, tel. mob.
+
Government debt – is consisted from all obligations, which demand payment or interest pay-
ments and is connected to main debtor to creditor relations, gov. debt
+
Budget – government balance, public finance balance as a percentage of net lending (+) / net 
borrowing (–) and is calculated as public finance salary minus expenses, budget
+
Savings-gross national savings as a percentage of GDP, joint national savings are defined as 
public and private savings and as a percentage of nominal GDP, savings
+
Inflation – influences changes in living standard through changes in prices, inflation –
Primary school enrolment – is a stage, which suits child relationship (as it is described in na-
tional educational system), who are enrolled to school in population of official schooling age, 
primarysch
+
Rating creditable of country –as assessing the probability of sovereign debt default, rating +
Table 1: Expected sign of correlation between GCI and basic requirements (continued)
Source: WEF (2014, 2017) Country economy profiles, 99-104.
4.2 Efficiency enhancers and global 
competitiveness
To test the relationship between global competitiveness 
and efficiency enhancers, Table 2 presents the sub-in-
dex efficiency enhancers variables that are based on WEF 
(2014). It is expected that these variables can be important 
in correlation with GDP per capita with an influence on 
GCI. Therefore, we check our assumption with expected 
theoretically positive signs of correlations.
4.3 Innovation and business 
sophistication and global 
competitiveness
Innovation and business sophistication give signs of spe-
cialization and contribute to a bigger effectiveness in pro-
duction of goods and services and increases quality of 
business performance, especially in mutual relationships, 
for example clusters development with geographical con-
centration of companies. Innovation means competence 
for innovations, knowledge and labour force experiences, 
technological innovations, namely availability of techno-
logical products, scientists, and patents.
Important indicators are value chains (companies of 
trade and production), clusters development and innova-
tions. Clusters development with geographical concen-
tration of companies can be linked with transactions and 
collaboration between companies, development of com-
munication technologies, social and cultural relationships 
between research institutions and universities.
Table 3 presents expected signs on correlation between 
innovation and business sophistication variables and GCI. 
Focus is on researches, universities-industries collabora-
tion in researches and clusters development, in correlation 
with GCI. Clusters development stage can positively influ-
ence on GCI as well as can geographical concentration of 
companies increase productivity of domestic competitors 
and increase rivalry. 
5 Empirical Analysis and Results
With correlation analysis is investigated the relationships 
between GCI and their explanatory variables for the EU–
28 countries. The GCI and explanatory indicators – basic 
requirements, efficiency enhancers, and innovation and 
business sophistication indicators – are taken from the 
WEF reports (2014-2015 and 2017-2018). The validation 
of the set two hypotheses is assessed by the applied regres-
sion and correlation analyses. The variables are grouped 
into three sub-index pillars. To evaluate the validation of 
the model it is used the regression analysis and correlation 
analysis, which are based on 56 observations, and assessed 
is the validation of the model for the EU-28 countries.
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Organizacija, Volume 53 Issue 1, February 2020Research Papers
Table 2: Expected sign of correlation between GCI and variables of efficiency enhancers
Variables of efficiency enhancers                                                                Sign of correlation
Import in % of GDP, import/GDP +
GDP per capita at standard of  purchasing  power parity (in PPP),  gdpp +
Secondary education, share of enrolment in higher education ISCED 2, ISCED 3, second.educ +
Tertiary education, enrolment in %, share of enrolment in tertiary education level ISCED 5, 6, tert. 
educ (in %) +
Quality of educational system, qual. educ +
Intensity of local competition, loc. compet. +
Availability of technology – in what scope they are technologies available in country, tech.avail +
Absorption of technologies, in what range company accepts new technologies, absorb. tech +
Direct investments and technological transfers, in what extent FDI brings new technologies, nti-
transf +
Internet users, (in %), internet +
Gross domestic product GDP (in PPP), valued in standards of  PPP in  billions of dollars, GDP +
Domestic market, index of size of local market, aggregate value of GDP in value of import of goods 
and services minus value of export of goods and services,  dom. market +
Foreign market, index of size of foreign market is valued as   aggregate value of export of goods, 
foreign. market +
Export in % of GDP, export/GDP +
Import in % of GDP, import/GDP +
Source: WEF (2014, 2017) Country economy profiles, 99-104.
Table 3: Expected sign of correlation between GCI and variables of innovation and business sophistication
Variables of innovation and business sophistication Sign of correlation 
GDP per capita at standard of PPP, gdpp +
State of clusters development – geographical concentration of companies, suppliers, producers,  
cluster develop +
Value chains breadth – companies trade and production, chain +
Innovation capacity, to what extent do companies have the capacity to innovate - capac. of inov. +
Quality of research institutions assesses the prevalence and standing of private and public research 
institutions, qual. of research +
Expenses for research in companies, to what extent do companies invest in research and develo-
pment,  expenditure on research and development (R&D) as a percentage of GDP,  expenses for 
research +
Universities-industry collaboration in researches, to what extent do business and universities colla-
borate on research and development (R&D), univind +
Government procurement of technological products, to what extent do government purchasing deci-
sions foster innovation, gov.proc. +
Availability of scientists, scientists +
Number of registered patents, patent +
Source: WEF (2014, 2017) Country economy profiles, 99-104.
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Organizacija, Volume 53 Issue 1, February 2020Research Papers
5.1 Correlation analysis between GCI 
and basic requirement variables
Table 4 presents correlation coefficients between two pair 
of variables. Our focus is on the correlation coefficients 
with the GCI. Correlation coefficient points on the rela-
tionship between pair of individual variables. Higher cor-
relation coefficient means stronger relationship, which can 
be positive or negative.
Among higher positive correlation between chosen 
variables, these are: intellectual property, clean of cor-
ruption, governmental regulation, legal framework in dis-
putes, resolving disagreements, and infrastructure quality.
Table 4 presents the Pearson correlation coefficients 
for 17 considered basic requirements variables and their 
pairs of correlation with GCI. The highest correlation co-
efficients are with the following variables with the GCI: 
intellectual property (0.88), property rights (80.90), cor-
ruption (0.86), legal framework in disputes (0.83), qual-
ity of roads (0.73), rating (0.84), and primary education 
(0.76). GDP per capita also shows high correlation with 
GCI (0.67) as well as government regulation (0.63).
It is interesting to note that 9 variables are most ap-
propriate drivers of GCI, because correlation coefficient 
is over 0.4. Finally, the correlations between the GCI and 
some variables are very low: quality of ports and quality 
of air, mobile and fixed telephone lines, budget, savings, 
inflation, and government debt.
5.2 Correlation analysis between GCI 
and efficiency enhancers variables
Table 5 presents correlation coefficients between select-
ed efficiency enhancers variables and GCI. The highest 
correlation coefficients for GCI are with the following 
variables: technological absorption (0.94), availability or 
accessibility of technology (0.93), quality of educational 
system (0.84), local competitiveness (0.80), and secondary 
education (0.76). It is interesting to note that GDP per cap-
ita also shows high correlation with GCI (0.67) as well as 
domestic markets (0.62), and foreign markets (0.77).
In all other cases the correlation coefficients between 
GCI and investigated variables are less than 0.5 or very 
low. For example, indicators of tertiary education do not 
affect significantly on GCI (0.43). It is surprisingly that 
variable enrolment into tertiary education has correlation 
coefficient with GDP per capita only 0.07, and internet 
users they also show on very low correlation with GCI 
(0.12). There is a negative correlation coefficient between 
tertiary education and variables of import/GDP. There ex-
ists also very low correlation between GCI and variables 
of export/GDP and internet users (0.12).
A correlation coefficient is lower between GCI and the 
share of import/GDP (0.19), and between GDP per capita 
and the share of export/GDP (0.21).
5.3 Correlation analysis between 
GCI and innovation and business 
sophistication variables
Table 6 presents correlation coefficients between GCI and 
selected innovation and business sophistication variables. 
There is a strong correlation of GCI with all included var-
iables, namely with GDP per capita (0.64), with clusters 
development (0.82), with chains (0.85), with capacity of 
innovations (0.91), with research expenses (0.94), with 
collaboration of companies and universities-industries 
collaboration in researches (0.89), and with patent of sup-
pliers (0.86). However, the correlation coefficient is lower 
with availability of scientists (0.48). In addition, there is 
a strong correlation between variables of chains and de-
velopment of clusters (0.89). If the cluster development is 
evolving and local suppliers are collaborating, then pro-
portionately the role chains is increasing. 
To sum up, innovation and business sophistication var-
iables are strongly correlated with GCI than with variables 
of the other two sub-indexes.
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Organizacija, Volume 53 Issue 1, February 2020Research Papers
Pearson C
orrelation C
oeffi
cients
Global index 
competitivi-
te, GCI
GDP per 
capita, gdpp
property 
rights, prop-
ertyr
intellectual 
property, 
intell. prop
coruption
government 
regulation, 
gov.reg
legal frame-
work
quality of 
road, qualr
quality of 
port, qualp
quality of 
airport, 
qualair
mobile 
telephone, 
elmob
fixed telele-
phone, telfix
budget
saving
inflation
governmen 
debt, gov.
debt
rating credit-
able, rating
primary 
school, 
primarsch
G
lobal index com
petitivite,  gci 
com
petitivite, G
C
I
1.000
.609*
.907*
.888*
.893*
.632*
.830*
.732*
.252
.300
.220
.307
.134
.436
.230
.083
.841*
.831*
G
D
P per capita, gdpp
.609*
1.000
.726*
.736*
.704*
.403
.674*
.562*
.064
.101
.162
.441
.169
.212
.053
-.006
.622*
.323
Property rights, propertyr
.907*
.726*
1.000
.969*
.944*
.719
.922
.720*
.326
.371
.088
.304
.159
.466
.306
.054
.829*
.627*
Intellectual property, intell. prop
.888*
.7363*
.969*
1.000
.927*
.674
.883
.730*
.307
.352
.064
.339
.197
.450
.285
.124
.806*
.617*
C
orruption
.893*
.704*
.944*
.927*
1.000
.614
.871
.740*
.169
.213
.233
.295
.093
.323
.148
.013
.765
.646*
G
overnm
ent regulation, gov.reg
.632*
.403
.719*
.674*
.614*
1.000
.752
.415
.796*
.817
-.386
-.126
.372
.843
.785
-.068
.619*
.388
Legal fram
ew
ork disput, leg.dis
.830*
.674*
.922
.883*
.871*
.752
1.000
.643*
.339
.378
.012
.203
.254
.464
.325
-.129
.813*
.458
quality of road, qualr
.732*
.562*
.720*
.730
.740*
.415
.643
1.000
.174
.215
.104
.480
-.113
.239
.154
.370
.548
.622*
quality of port, qualp
.252
.064
.326
.307
.169
.796
.339
.174
1.000
.998
-.747
-.337
.293
.944
.998
.075
.360
.115
quality of airport, qualair
.300
.101
.371
.352
.213
.817
.378
.215
.998*
1.000
-.730
-.306
.294
.949
.994
.093
.394
.158
m
obile telephone, telm
ob
.220
.162
.088
.064
.233
-.386
.012
.104
-.747*
-.730*
1.000
.284
-.123
-.625
-.750
-.120
.067
.362
fixed telelephone, telfix
.307
.441
.304
.339
.295
-.126
.203
.480
-.337
-.306
.284
1.000
-.183
-.260
-.351
.410
.110
.324
budget
.134
.169
.159
.197
.093
.372
.254
-.113
.293
.294
-.123
-.183
1.000
.341
.284
-.306
.254
-.096
saving
.436
.212
.466
.450
.323
.843*
.464
.239
.944
.949*
-.625
-.260
.341
1.000
.940
-.052
.547
.248
inflation
.230
.053
.306
.285
.148
.785
.325
.154
.998
.994*
-.750
-.351
.284
.940
1.000
.058
.352
.090
governm
ent debt, gov.debt
.083
-.006
.054
.124
.013
-.068
-.129
.370
.075
.093
-.120
.410
-.306
-.052
.058
1.000
-.238
.316
rating creditable. rating
.841
.622*
.829*
.806*
.765*
.619
.813
.548*
.360
.394
.067
.110
.254
.547
.352
-.238
1.000
.516
prim
ary school, prim
arsch
.831
.323
.627*
.617
.646*
.388
.458
.622*
.115
.158
.362
.324
-.096
.248
.090
.316
.516
1.000
Table 4: C
orrelation m
atrix betw
een G
lobal C
om
petitiveness Index (G
C
I) and basic requirem
ent variables for the EU
-28 countries, data for 2014 and 2017
**Statistically significant at 5%
 significance level. Source: A
uthors’ calculations
45
Organizacija, Volume 53 Issue 1, February 2020Research Papers
Global index 
competitivite 
GCI
GDP per 
capita gdpp
Secondary 
education 
second. edu
 Tertiary 
education 
tert.educ
 quality of 
education 
qual.educ
import/GDP
Local 
compet, loc 
comp.
Availability 
of technol-
ogy, tech.
avail.
 Absorption 
of technolo-
gies, absorb.
tech.
Direct 
investments- 
technology 
nti-transf
Internet us-
ers internet
 domestic 
market
 foreign 
market 
 export/GDP
G
lobal index com
petitivite, G
C
I
1.000
.673*
.764
.431
.842*
.199
.803*
.931*
.940*
.799*
.121
.627*
.777*
.214
G
D
P per capita, gdpp
.673*
1.000
.460
.075
.662*
.340
.430
.635*
.650*
.558*
.600*
.321
.456
.406
secondary education, second. edu
.764*
.460
1.000
.582
.646*
.140
.583*
.749*
.708*
.659*
-.022
.495
.617*
.154
 tertiary education, tert.educ
.431
.075*
.582*
1.000
.315
-.169
.377
.411
.402
.219
-.392
.390
.406
-.170
 quality of education, qual.educ.
.842*
.662*
.646*
.315
1.000
.229
.630*
.828*
.851*
.678*
.098
.330
.464
.229
im
port/G
D
P
.199
.340
.140
-.169
.229
1.000
.349
.270
.269
.481
.501*
-.341
.014
.984
Local com
petition, loc com
p.
.803*
.430
.583*
.377
.630
.349
1.000
.808
.819*
.776*
.066
.464
.663*
.322
availability of technology, tech.avail.
.931*
.635*
.749*
.411
.828*
.270
.808*
1.000
.967*
.802*
.157
.491
.658*
.277
absorption of technologies, absorb.tech. 
.940*
.650*
.708*
.402
.851
.269
.819
.967
1.000
.821*
.151
.466
.638*
.275
direct investm
ents- technology, nti-transf
.799*
.558*
.659*
.219
.678*
.481
.776*
.802*
.821*
1.000
.214
.407
.645*
.489
Internet users, internet
.121
.600*
-.022
-.392
.098*
.501
.066
.157
.151
.214
1.000
-.192
-.053
.538
D
om
estic m
arket, dom
.m
arket
.627*
.321
.495
.390
.330
-.341
.464
.491
.466
.407
-.192
1.000
.924*
-.295
Foreign m
arket, foreig. m
arket
.777*
.456
.617*
.406
.464
.014
.663*
.658*
.638*
.645*
-.053
.924
1.000
.063
export/G
D
P
.214
.406
.154
-.170
.229
.984*
.322
.277
.275
.489
.538*
-.295
.063
1.000
Table 5: C
orrelation m
atrix betw
een G
lobal C
om
petitiveness Index (G
C
I) and efficiency enhancers variables for the EU
-28 countries, data for 2014 and 2017
**Statistically significant at 5%
 significance level. Source: A
uthors’ calculations
46
Organizacija, Volume 53 Issue 1, February 2020Research Papers
Pearson correlation coeffi
cients
Global index 
competitivit 
GCI
GDP per 
capita gdpp
state of 
clusters 
develop-
ment, cluster 
develop.
value chains 
breadth 
chain
Innovation 
capacity 
innovation 
capac. inov,
quality of 
research 
institu-
tions qual. 
research
expenses for 
research in 
companie, 
expens.
research
universi-
ties-industry 
collaboration 
in researches 
- univind
govern.
procurement 
tehnology, 
gov proc.
availability 
of scientists 
scients
number 
registered 
patents, 
patent
G
lobal index com
petitivit, 
G
C
I 
1.000
.641*
.828*
.857*
.918*
.872*
.943*
.890*
.742*
.489
.867*
G
D
P per capita, gdpp
.641*
1.000
.659*
.640*
.665*
.517*
.674*
.602*
.620*
.306
.561*
state of clusters developm
ent 
cluster develop
.828*
.659*
1.000
.896*
.828*
.770*
.828*
.801*
.667*
.540*
.717*
value chains breadth, chain
.857*
.640*
.896*
1.000
.855*
.765*
.874*
.764*
.584*
.566*
.788*
innovation capacity, capac. 
inov
.918*
.665*
.828*
.855*
1.000
.825*
.950*
.813*
.663*
.445
.791*
quality of research institu-
tions qual. research
.872*
.517*
.770*
.765*
.825*
1.000
.854*
.901*
.593*
.518*
.724*
expenses for research in com
-
panies, expens.research
.943*
.674*
.828*
.874*
.950*
.854*
1.000
.855*
.676*
.496
.867*
universities-industry collabo-
ration in researches univind
.890*
.602*
.801*
.764*
.813*
.901*
.855*
1.000
.746*
.559*
.777*
governem
ent procurem
ent 
technology, gov proc.
.742*
.620*
.667*
.584*
.663*
.593*
.676*
.746*
1.000
.362
.614*
availability of scientists, 
scientists
.489
.306
.540*
.566*
.445
.518*
.496
.559*
.362
1.000
.557*
num
ber of registered patents, 
patent
.867*
.561*
.717*
.788*
.791*
.724*
.867*
.777*
.614*
.557*
1.000
Table 6: C
orrelation m
atrix betw
een G
C
I and innovation and business sophistication variables for the EU
-28 countries, data for 2014 and 2017
**Statistically significant at 5%
 significance level. Source: A
uthors’ calculations
47
Organizacija, Volume 53 Issue 1, February 2020Research Papers
5.4 Regression Analysis
The aim of the linear regression analysis is to determine 
the association between the GCI and two explanatory var-
iables for cluster development with geographical concen-
tration of companies, and universities-industries collabo-
ration. Regression analysis is limited to only two variables 
and we want to test the hypotheses. We used partial re-
gression analysis when we selected only one explanatory 
variable. Thus future studies could be focused on multiple 
explanatory variables of GCI in regression analysis. Data 
for the analysed variables are obtained from WEF (2014) 
and WEF (2017).
The variables are grouped into two pillars (11th and 
12th) innovation and business sophistication. Explanatory 
variables were selected individually because universities 
define the competitiveness of technological innovation, 
while the clusters development with geographical concen-
tration defines the competitiveness of non-technological 
innovation.
The regression analysis is based on 56 observations, 
which correspond to EU-28 country observations for the 
two analysed years 2014 and 2017.
Table 7 presents the GCI association with companies 
and universities-industries collaboration in researches. 
Determination coefficient R2= 0.793 shows that 79% of 
GCI variability is explained with companies and universi-
ties-industry collaboration in researches variable. 
Coefficient of correlation (R=0.891) suggests on a 
strong linear relationship between companies and univer-
sities-industry collaboration in researches and GCI.
Table 8 presents the analysis of variance (ANOVA). 
At a significance level less than 1% (p = 0.000), the ex-
planatory variable universities-industry collaboration in 
researches is statistically significant. 
F-test shows that there is a linear dependence between 
variables (F=203.222), and variable is statistically highly 
significant. If the p-value is less than the critical signif-
icance level (p<0.005), then sample data provides suffi-
cient evidence to conclude that the regression model fits 
the data.
Furthermore, Table 9 presents regression coefficients 
with t-test and p-value, and statistic characteristics of the 
regression model.
Model Summaryb
Model R R Square Adjusted R 
Square
Std. Error of the 
Estimate
Change Statistics
R Square 
Change
F Change df1 df2 Sig. F 
Change
1 0.891a 0.793 0.789 0.22681 0.793 203.222 1 53 0.000
a. Predictors: (Constant). Universeind
b. Dependent Variable: GCI
Table 7: Model Summary
ANOVAa
Model Sum of Squares Df Mean Square F Sig.
1 Regression 10.455 1 10.455 203.222 0.000b
Residual 2.727 53 0.051
Total 13.181 54
a. Dependent Variable: GCI
b. Predictors: (Constant). Universeind
Table 8: ANOVA
48
Organizacija, Volume 53 Issue 1, February 2020Research Papers
ANOVA with F-test shows that clusters development 
with geographical concentration of companies is statisti-
cally significant at 1% level (p<0.001) (Table 11). 
The research hypothesis about the existence of strong 
positive correlation between GCI and clusters develop-
ment with geographical concentration of companies can 
be accepted. This is further confirmed by statistically sig-
nificant regression coefficient (Table 12). 
Regression line value is: GCI= 2.534+0.533cluster. 
The regression equation shows that the regression coeffi-
cient that is pertained to cluster development variable is 
0.533. If clusters development with geographical concen-
tration of companies increases by 1 scale, then GCI in-
creases by 0.533 scale of GCI, ceteris paribus. 
The regression line value is GCI=2.679+0.495 univer-
seind. If companies and universities-industry collabora-
tion in researches increases for 1 scale, then GCI increases 
by 0.495 scale, ceteris paribus. Table 10 shows to what 
extent GCI is associated with clusters development with 
geographical concentration of companies. Coefficient of 
correlation (R=0.831) shows a linear relationship between 
clusters development and GCI. Coefficient of determina-
tion (R2=0.69) shows that regression model fits the data: 
69% of variability in GCI is explained with clusters de-
velopment with geographical concentration of companies. 
Higher cluster development values are associated with 
higher GCI. 
Table 9: Regression Coefficients
Coefficientsa
Model Unstandardized Coefficients Standardized Coefficients t-test Sig.
B Std. Error Beta
1 (Constant) 2.679 0.151 17.735 0.000
Universeind 0.495 0.035 0.891 14.256 0.000
a. Dependent Variable: GCI
Table 10: Model Summary
Model Summaryb
Model R R Square Adjusted R 
Square
Std. Error of the 
Estimate
Change Statistics
R Square 
Change
F Change df1 df2 Sig. F 
Change
1 0.831a 0.690 0.684 0.27766 0.690 117.971 1 53 0.000
a. Predictors: (Constant). Cluster
b. Dependent Variable: GCI
ANOVAa
Model Sum of Squares Df Mean Square F Sig.
1 Regression 9.095 1 9.095 117.971 0.000b
Residual 4.086 53 0.077
Total 13.181 54
a. Dependent Variable: GCI
b. Predictors: (Constant). Cluster
Table 11: ANOVA
49
Organizacija, Volume 53 Issue 1, February 2020Research Papers
6 Discussion
The correlation coefficients between three sub-indexes 
variables – basic requirements, efficiency enhancers, and 
innovation and business sophistication –  and GCI for 
the EU-28 countries indicated a positive correlation. The 
stronger positive correlation of GCI is with innovation and 
business sophistication sub-index than other two sub-in-
dexes. Some variables of the sub-index basic requirements 
have a weak correlation such as quality of port and quality 
of air, mobile and fixed telephone lines, budget, savings, 
inflation, and government debt. In addition, a lower corre-
lation between GCI and sub-index of efficiency enhancers 
is for export/GDP, import/GDP, and internet users. While 
there are differences in the results, all three groups of 
sub-indexes variables are in a significantly positive corre-
lation with GCI. This implies that improvements in basic 
requirements, efficiency enhancers, and innovation and 
business sophistication are crucial to increase GCI.
The validity of the H1 cannot be rejected on the im-
portance of the innovation and business sophistication 
sub-index for GCI. In addition, the regression analysis 
confirmed the H2 on the importance of universities-indus-
tries collaboration in researches and cluster development 
for GCI (p-value is less than the critical significance level, 
p<0.005).
To increase global competitiveness, a greater focus 
should be given to the importance of innovation and busi-
ness sophistication at different levels. In addition, global 
competitiveness can be improved through universities-in-
dustries collaboration in researches, which supported the 
networking approaches in ongoing funding of research in 
some of the EU-28 countries to contribute to rise of global 
competitiveness.
The relevance of our study is that rises awareness for 
policy and decision makers on the importance of drivers of 
global competitiveness and possible ways for improving 
the EU-28 country’s global competitiveness. It can be rel-
evant for science, policy formation and managerial prac-
tices, that enhance innovation and business sophistication 
in relation to research and collaboration of companies and 
universities-industry, research institution management, 
and policy of higher education that create knowledge and 
training.
The scientific contribution of the study is that devel-
oped relationships between three pillar groups of sub-in-
dexes variables of GCI and competitiveness. The paper 
contributes to the literature of global competitiveness, by 
examining the role sub-indexes of competitiveness for 
global competitiveness of the EU-28 countries, pointing 
out the influence of universities-industry collaboration in 
research and cluster development with geographic con-
centration of companies. Our results are consistent with 
Rusu and Roman (2018) on the relationships between the 
sub-indexes of competitiveness and GCI. Paraušić et al. 
(2014) found that cluster development and innovation and 
business sophistication can have a significant influence on 
national competitiveness in emerging markets and devel-
oping countries.
Therefore, cluster developments and universities-in-
dustries collaboration in researches can have important 
role in the improving global competitiveness for the EU-
28 countries, but it can also require well targeted invest-
ments in uncertain global environment.
7 Conclusion
The paper contributes to analyses of drivers of global 
competitiveness. Different drivers can explain global com-
petitiveness in the EU-28 countries. To investigate this 
research question, we have applied the correlation and re-
gression analyses in the years 2014 and 2017. There exists 
strong correlation especially between the GCI and expens-
es for research, innovation capacity, universities-industries 
collaboration in researches, and patents. All these is relat-
ed to investments, organization and management of R&D, 
and innovation and business sophistication activities. The 
relationships between GCI and variables in the sub-index 
of innovation and business sophistication are stronger than 
in other two groups of sub-indexes for basic requirements 
and efficiency enhancers. In the third group of indicators 
for innovation and business sophistication, there is a strong 
Coefficientsa
Model Unstandardized Coefficients Standardized Coefficients t-test Sig.
B Std. Error Beta
1 (Constant) 2.534 0.211 12.018 0.000
Cluster 0.533 0.049 0.831 10.861 0.000
a. Dependent Variable: GCI
Table 12: Regression Coefficients 
50
Organizacija, Volume 53 Issue 1, February 2020Research Papers
correlation of the GCI with cluster development, capacity 
of innovations, chain value, with quality of research in-
stitution, with research expenses, with universities-indus-
tries collaboration in researches, government procurement, 
and with patent of suppliers. It is interesting, that very low 
correlation is found only with availability of scientists and 
engineers.
The regression analysis confirmed H1: Relationship 
exists between the level of the EU-28 countries competi-
tiveness and indicators of innovation and business sophis-
tication, but weak relationship exists between the EU-28 
countries competitiveness and sub-indexes of other two 
pillars groups (basic requirements and efficiency enhanc-
ers). Therefore, H1 cannot be rejected, because the exist-
ence of strong relationship between the level of GCI in 
the EU-28 countries and the third group of indicators of 
sub-index, i.e., innovation and business sophistication. Im-
provements in innovation and business sophistication can 
lead to increases in GCI.
The statistical analysis showed that clusters develop-
ment with geographic concentration of companies and uni-
versities-industry collaboration in researches have strong 
positive influence on the GCI. The regression analysis con-
firmed H2: University-industry collaboration in researches 
and clusters development with geographical concentration 
of companies are statistically significant drivers of the GCI 
in the EU-28 countries.
Therefore, global competitiveness of the EU-28 coun-
tries can be improved by widespread clusters development 
with geographical concentration of companies and other 
drivers of innovation and business sophistication on the 
international market, as well as with improved universi-
ties-industry collaboration in researches. This can have 
policy implications for science and universities, innova-
tion and business sophistication, and managerial practices 
for doing business in companies.
Our study has more limitations. The analysis is limited 
to two WEF data calculations/reports in 2014 (2014-2015) 
and in 2017 (2017-2018) with comparable indicators. 
Among study limitations, the study investigated only two 
variables in the regression analysis. Therefore, an issue 
for further research is to expand analysis with investiga-
tion of dynamics in longer time-frame in the multivariate 
analysis. The correlation and regression analyses are lim-
ited to the sub-indexes of GCI. In addition, the regression 
analysis is limited to partial analysis of two explanatory 
variables, the companies and universities-industry collab-
oration in researches, and clusters development with geo-
graphic concentration of companies for the two analysed 
years. In the future research, first, the panel data analysis 
for more years can be applied. Second, the model specifi-
cation can be extended on variables of higher education. 
Finally, it could be applied cluster analysis for three groups 
of the EU-28 countries according to the stage of WEF de-
velopment. As the EU-28 countries are at different stages 
of WEF development, individual factors can have different 
meanings for the competitiveness of individual countries. 
Therefore, an issue for research in future is to introduce the 
heterogeneity of the EU-28 countries.
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52
Organizacija, Volume 53 Issue 1, February 2020Research Papers
Milja Marčeta, finished master’s degree in International 
Economics from the Faculty of Economics, University of 
Ljubljana and master’s degree in Economic Informatics 
from the Faculty of Economics, University of Ljubljana. 
She works for Ministry of Education, Science and Sport 
in Ljubljana, Slovenia.
Štefan Bojnec, is Professor of Economics and 
Head of Department of Economics at the Faculty of 
Management, University of Primorska, Slovenia. His 
bibliography comprises around 1.700 bibliographic 
records, with around 290 original scientific articles 
in international scientific journals with a peer review, 
around 135 publications in journals included in JCR/
SNIP/WoS journals and more than 150 publications in 
journals included in Scopus. In 2008 he received the 
Slovenian state Zois recognition for important scientific 
achievements in the field of economics.
Dejavniki globalne konkurenčnosti v državah Evropske unije v letih 2014 in 2017
Ozadje in namen: Glavni namen raziskave je ugotoviti ključne dejavnike globalnega indeksa  konkurenčnosti (GCI) 
v državah Evropske unije (EU-28) z vidika globalne konkurenčnosti države: institucije, makroekonomsko okolje, 
infrastruktura, visoko šolstvo, učinkovitost trga, velikost trga, tehnološka pripravljenost, inovacije in poslovna prefi-
njenost.
Metodologija: Članek raziskuje globalno konkurenčnost držav EU-28 z uporabo GCI v obdobjih 2014–2015 in 2017–
2018. Korelacijska analiza in regresijska analiza se uporabljata za testiranje postavljenih dveh hipotez.
Rezultati: Empirični rezultati so potrdili postavljeni hipotezi, da je GCI zelo pomembno pozitivno povezan z inova-
cijami in poslovno sofisticiranostjo, sodelovanjem med univerzami in industrijo v raziskavah in z razvojem grozdov.
Zaključek: Raziskava prispeva k literaturi o svetovni konkurenčnosti s preučevanjem razmerja podindeksov kon-
kurenčnosti držav EU-28. Poudarja na vpliv sodelovanja univerz in industrije pri raziskavah in razvoju grozdov z 
geografsko koncentracijo podjetij. Rezultati in ugotovitve so lahko pomembni za znanost, ekonomsko in raziskovalno 
politiko ter vodstvene prakse, ki povečujejo inovativnost in poslovno prefinjenost raziskav v sodelovanju podjetij, 
univerz, visokošolskih zavodov in odločevalcev. Implikacije raziskave so lahko pomembne za boljše razumevanje 
dejavnikov globalne konkurenčnosti držav EU-28.
Ključne besede: konkurenčnost, gospodarske dejavnosti, indeks globalne konkurenčnosti, inovacije