RFID Usage in European Enterprises: Cluster Analysis Approach

Abstract

The radio frequency identification (RFID) is one of the most promising new technologies of modern economy, with a high potential of improving the efficiency and productivity of enterprises. The main goal of this study is to determine the extent of RFID usage in European (EU) countries [The expansion for “EU” has been used as “European” throughout the article. Hence, please check and approve the edits.]and to estimate whether there is a relation between countries’ competitiveness and their levels of RFID usage. First, the trend of RFID usage among EU enterprises is analyzed for the years 2009, 2011, and 2014. Second, a cluster analysis is applied with the goal to create homogenous groups of countries according to the RFID application across different industries. Third, countries from different clusters were compared according to their competitiveness in 2014. Results revealed that enterprises in Europe differ substantially according to their RFID usage. Also, RFID usage is not evenly distributed among countries across different industries, since some countries were leaders in RFID usage in one industry while lagging behind in RFID usage in other industries. Finally, countries from clusters with a higher RFID usage level also have a higher level of technological readiness and innovation, while smaller differences were found in business sophistication.

Full text

Research Article
RFID usage in European enterprises
and its relation to competitiveness:
Cluster analysis approach
Mirjana Pejic
´Bach
1
, Jovana Zoroja
1
, and Michalis Loupis
2
Abstract
The radio frequency identification (RFID) is one of the most promising new technologies of modern economy, with a high
potential of improving the efficiency and productivity of enterprises. The main goal of this study is to determine the extent
of RFID usage in European (EU) countries and to estimate whether there is a relation between countries’ competitiveness
and their levels of RFID usage. First, the trend of RFID usage among EU enterprises is analyzed for the years 2009, 2011,
and 2014. Second, a cluster analysis is applied with the goal to create homogenous groups of countries according to the
RFID application across different industries. Third, countries from different clusters were compared according to their
competitiveness in 2014. Results revealed that enterprises in Europe differ substantially according to their RFID usage.
Also, RFID usage is not evenly distributed among countries across different industries, since some countries were leaders
in RFID usage in one industry while lagging behind in RFID usage in other industries. Finally, countries from clusters with a
higher RFID usage level also have a higher level of technological readiness and innovation, while smaller differences were
found in business sophistication.
Keywords
Radio frequency identification, clustering, K-means, enterprises, European countries, competitiveness, global competi-
tiveness report
Date received: 11 September 2016; accepted: 27 November 2016
Introduction
Usage of the latest information and communication tech-
nologies (ICTs) provides many benefits for enterprises,
for example, better access to information, lower costs,
understanding customers’ needs, higher product/service
quality, and competitive advantage.
1
In order to enhance
competitiveness and efficiently compete on the world
market, enterprises have to implement and use the latest
inventions in ICTs. Radio frequency identification (RFID)
presents an innovative ICT that can improve the current
business model, transform business processes, and support
decision-making processes.
1,2
RFID technology has been used since World War II,
3
but it has caught the attention of large global enterprises,
for instance, Wal-Mart in the United States, in the last few
decades. After other large global retailers, such as Tesco,
Metro, and P&G, started to use RFID technology, its
importance and benefits have been recognized worldwide.
The usage of RFID has increased in the last decade in
other industries as well, and nowadays, RFID is used in
logistics, medication treatment, finance, supply chain,
retail, defense industries, manufacturing, and agriculture.
Currently, the most positive impact of RFID usage is found
in supply chain management, inventory management, and
1
Department of Informatics, Faculty of Economics and Business,
University of Zagreb, Croatia
2
Central Greece Institute of Technology, Athens, Greece
Corresponding Author:
Mirjana Pejic
´Bach, Sveuc
ˇilis
ˇte u Zagrebu, Trg J. F. Kennedyja 6, Zagreb
10000, Croatia.
Email: mpe jic@e fzg.hr
International Journal of Engineering
Business Management
Volume 8: 1–11
ªThe Author(s) 2016
DOI: 10.1177/1847979016685093
journals.sagepub.com/home/enb
Creative Commons CC BY: This article is distributed under the terms of the Creative Commons Attribution 3.0 License
(http://www.creativecommons.org/licenses/by/3.0/) which permits any use, reproduction and distribution of the work without
further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/
open-access-at-sage).

warehouse and retail stores, thus in industries in which mov-
ing objects have to be tracked,
4
with the promising prospect
of its increased usage in the future. However, the enterprises
that stagnate with the usage of RFID technology as one of
the main tools toward the full utilization of the Internet of
things could endanger their market position both in short-
and long-term.
The goal of the article is threefold. First, we want to
analyze the usage of RFID among enterprises in European
(EU) countries in years 2009, 2011, and 2014. Second, we
want to explore whether there are differences among EU
countries according to the usage of RFID technology across
different industry sectors, using cluster analysis. Third, we
aim to investigate whether there are differences in compe-
titiveness between different countries according to their
RFID usage, using the global competitiveness index, and
indicators from its pillars—the 9th pillar: technological
readiness; the 11th pillar: business sophistication; and the
12th pillar: innovation.
5
In order to accomplish the goal of the article, we used
the data about RFID usage in enterprises in 30 EU coun-
tries. Data were collected from the European Commission
Statistics Database (Eurostat) for the years 2009, 2011, and
2014. We analyzed the changes of RFID usage in 2009 and
2011, and 2011 and 2014. A cluster analysis was conducted
to organize EU countries into sensible groupings using
Eurostat data for 2014, according to RFID usage in enter-
prises.
6
The Kruskal–Wallis test was used for the compar-
ison of the competitiveness of cluster countries using the
global competitiveness index and indicators from its pil-
lars—the 9th pillar: technological readiness; the 11th pillar:
business sophistication; and the 12th pillar: innovation.
5
The article is organized as follows. After the introduc-
tion as the first part of the article, the second part presents
an overview of RFID technology and its usage in enter-
prises. In the third part of the article, data and methodology
have been described. Results are described in the fourth
part and discussed in the fifth part. The last part concludes
the article.
RFID technology and its usage
across industries
RFID is a form of wireless communication using radio
waves, which enables automatic identification of objects,
collecting data about them, and entering those data into
enterprise information systems with little or without human
help.
7–9
RFID enables more effective operations, greater
forecasting accuracy, reduced labor costs, shorter transpor-
tation time, and better quality of customer service and man-
agement of inventory
7,10–12
through real-time data collection
and object identification.
13
The RFID implementation has
the best results if it is applied in several enterprises that are a
part of a group or work together.
1,8,14
Before the implementation of RFID, enterprises should
conduct several analytical studies: future usage planning,
Strengths, Weaknesses, Opportunities,and Threats (SWOT),
and cost–benefit analysis.
1
Several challenges can be men-
tioned regarding RFID implementation: integration of RFID
withtheexistingITapplications,
15
a lack of technical exper-
tise and complexity of the technology,
16
and a shortage of
RFID skills and a lack of standardization.
17
Therefore, the
success of the RFID implementation in enterprises depends
upon several factors, for example, data management, existing
IT infrastructure, size of enterprises, business value, and
implementation complexity.
18
RFID technology has a positive impact on business perfor-
mance in numerous industries, such as production, distribution,
and retailing.
19
However, it is also widely used as a means
toward the development of public operations, such as smart
cities. In this article, we have focused on RFID usage in the
following seven industries: manufacturing, electricity, con-
struction, trade, transportation, accommodation, and ICTs.
RFID technologies enable manufacturing organizations
to better handle raw materials, work in progress, products,
and inventories, which have a positive impact on manu-
facturing organizations and the supply chain.
20,21
There is
also research where authors confirmed that RFID usage
significantly influences inventory, quality of products,
delivery speed, customer satisfaction, and accuracy of
information.
22
In relation to the construction sector, RFID is mostly used
in outdoor environment since it enables timely tracking of
the project status, location of materials, workforce, equip-
ment resources, earthmoving operations, and safety hazards
predicting.
23
Additionally, in the construction sector, RFID
technology is mostly used for material management, such
as
24
locating construction materials, maintenance, construc-
tion quality, and employees’ management.
RFID technologies used in the retail industry allow
cost saving, improved service quality, reduced prices for
clients, and better control of distribution.
25–27
Advantages of
RFID usage in the retail industry are improving efficiency,
accuracy, and security in the supply chain and inventory
management.
26
RFID technology has many applications in transporta-
tion industry, for example, reduction of operating costs,
status and location monitoring, vehicle tracking, improve-
ment of the work accuracy, and better visibility of ongoing
operations.
28
Another advantage is a lower possibility of
cargo being stolen, lost, or destroyed.
29
RFID usage in tourism could be grouped into four cate-
gories.
30
The first group of RFID usage is human tracking
and control and refers to customer loyalty management,
tracking people with special needs and airport security.
31,32
The second group of RFID applications in tourism includes
assets and valuables tracking systems, and it refers to lug-
gage tracking and food management.
33,34
The third group
of RFID usage in tourism refers to contactless payment
systems, and it is related to payments in hotels and keyless
room entry.
35
In the last group, RFID-based information,
RFID could be used for sightseeing tours.
36
2International Journal of Engineering Business Management

RFID benefits are recognized and used the most in the
ICT sector.
37,38
For example, Wu and coauthors have pre-
sented the design and development of an RFID-based
health-care information system.
13
Barjis and Fosso Wamba
present RFID usage in health care management regarding
instant access to patient information, handling laboratory
samples, and locating devices.
1
Methodology
Sample description
We have analyzed three sets of data: (1) the usage of RFID
technologies in enterprises in the selected EU countries in
2009, 2011, and 2014; (2) the usage of RFID technologies
in enterprises from the selected industry sector in the
selected EU countries in 2014; and (3) competitiveness of
cluster countries in 2014, using the global competitiveness
index and indicators from its pillars—the 9th pillar: tech-
nological readiness; the 11th pillar: business sophistication;
and the 12th pillar: innovation.
5
First, data on RFID usage were collected for 30 EU
countries (EU countries—Iceland, Norway, and FYR
Macedonia) from the Eurostat database,
39
for which the
data were available. Data were collected for the years
2009, 2011, and 2014. The enterprises were asked whether
in their business operations, they used: (1) an automatic
identification method to store and remotely retrieve data
using RFID tags or transponders or (2) an RFID tag, which
is a device that can be applied to or incorporated into a
product or an object and transmits data via radio waves.
6
If one of those usages is present in the enterprise, it is
considered that the enterprise is using RFID technology.
Second, data on the usage of RFID in enterprises in the
selected EU countries in 2014 across several industry sec-
tors were collected from the Eurostat database. Seven dif-
ferent industry groups were selected for the purpose of the
analysis due to the widespread RFID usage (manufacturing,
electricity, gas, steam, air conditioning and water supply,
construction, wholesale and retail trade, repair of motor
vehicles and motorcycles, transport, accommodation, and
information and communication). Data for both data sets
were collected as the percentage of enterprises which have
10 or more employees and use RFID technology.
The third data set is used for measuring the competitive-
ness of the selected EU countries. The Global Competitive-
ness Report defines competitiveness as ‘‘the set of
institutions, policies, and factors that determine the level
of productivity of a country,’’
5
and it measures the compe-
titiveness with the overall competitiveness index, three
subindices, and 12 pillars.
Data were collected for the competitiveness dimensions
that could be related to the usage of RFID technology—the
9th pillar: technological readiness; the 11th pillar: business
sophistication; and the 12th pillar: innovation and the glo-
bal competitiveness index. The competitiveness indicators
are defined and collected by the World Economic Forum,
and the data are collected for the year 2014.
5
Three types of
data are collected: (1) composite indices (e.g. the 9th pillar:
technological readiness), (2) data on opinions of the
respondents of the firms representing the main sectors of
the economy (agriculture, manufacturing industry, nonma-
nufacturing industry, and services) collected using the
Likert-type scale 1–7 (e.g. 9.01 availability of the latest
technologies, 1–7), and (3) official statistical data (e.g.
9.07 mobile broadband subscriptions/100 inhabitants).
Statistical methods
The process of the statistical analysis consists on the fol-
lowing four steps:
Step 1: Analysis of trends in RFID usage in EU
countries in 2009, 2011, and 2014.
Step 2: Exploratory analysis of RFID usage in EU
countries in selected industry sectors in 2014.
Step 3:K-means cluster analysis of RFID usage in
EU countries in selected industry sectors in 2014.
Step 4: Kruskal–Wallis test of the differences in com-
petitiveness between clusters as the result of the step3.
In step 1, we have used a descriptive statistical analysis
in order to give an evaluation of the trends in RFID usage in
2009, 2011, and 2014. Step 2 refers to the exploratory
analysis of RFID usage in EU countries in selected industry
sectors in 2014, and it included a descriptive statistical
analysis and outlier detection. Step 3 refers to the usage
of the K-means cluster analysis
40
in order to organize EU
countries into homogenous groups for the year 2014, regard-
ing their usage of RFID across different industries. One of the
best ways to understand a large amount of data is to separate
them into homogeneous groups.
41,42
The cluster analysis is
one of the knowledge discovery techniques usedto find struc-
ture in data and to group data into clusters based on their
similarities. Application of cluster analysis can be found in
the following domains: data mining and knowledge discov-
ery, data compression and vector quantization, optimization,
finance, manufacturing, and medical organizations.
41,43
In
the step 3, data on the usage of RFID in 28 EU countries were
used for cluster determination. Using the nonhierarchical
K-means cluster analysis, countries were grouped regarding
indicators of competitiveness 9th, 11th, and 12th pillars. In
step 4, we used the Kruskal–Wallis test
44
in order to compare
identified competitiveness of cluster countries.
Results
Step 1: Analysis of trends in RFID usage in EU
countries in 2009, 2011, and 2014
Trends in RFID usage are presented in Figure 1. As
expected, usage of RFID increased in 2014 in comparison
to usage in 2011 and 2009. In 2009, enterprises from the
Bach et al. 3

Netherlands (9%) and Finland (8%) used the RFID tech-
nology the most. In several other countries (Germany,
Spain, Croatia, Austria, and Slovakia), about 4%of enter-
prises used RFID technology. For several EU countries,
data on the usage of RFID technology were not available
(Greece, Latvia, Malta, Iceland, and FYR Macedonia). The
situation in 2011 is almost the same as the situation in 2009.
The largest number of enterprises using RFID (8%) was in
Finland and Slovakia, followed by Croatia, Lithuania, and
Malta (7%). In 2014, RFID usage in enterprises was higher
than 15%in several countries (Belgium: 17%, Malta: 15%,
Austria: 18%, and FYR Macedonia: 15%). The highest
percentage of enterprises using RFID was in Finland
(21%). The lowest percentage of enterprises that used
RFID in 2014 was in Greece (4%).
Changes in RFID usage are presented in Figure 2. The
graph shows that changes between 2009 and 2011 are lower
than changes between 2011 and 2014. The comparison of
data from 2009 and 2011 indicates that there were no
changes regarding Finland, Romania, and Italy. The data
indicate that RFID usage was lower in 2011 than in 2009 in
France, Poland, and the United Kingdom (1%point), while
in the Netherlands, the decline of RFID usage among enter-
prises was 7%points. However, this data could be the result
of the different sample used. Since the usage of RFID is
still rather low, it is possible that the sample in those coun-
tries did not represent the population in the most represen-
tative manner. Comparing changes from 2011 to 2014, one
can conclude that there was a rather high increase in RFID
usage among countries. The highest progress was in the
highly developed EU countries (Belgium, Luxembourg, the
Netherlands, Austria, and Finland).
Step 2: Exploratory analysis of RFID usage in EU
countries in selected industry sectors in 2014
Table 1 presents descriptive statistics of RFID usage
according to the industry on the overall sample of
Figure 1. Trends in RFID usage in European countries in 2009, 2011, and 2014. Source: Authors’ calculation based on Eurostat data
(2016a). RFID: radio frequency identification.
Figure 2. Changes in RFID usage in European countries in 2009, 2011, and 2014. Source: Authors’ calculation based on Eurostat data
(2016a). RFID: radio frequency identification.
4International Journal of Engineering Business Management

enterprises from EU countries in 2014. The average grade
is the lowest for the construction industry (6.45), while the
highest average grade is for the ICT sector (22.14) and the
energy industry (18.39). Other four industries that have
almost the same average grade are the manufacturing
(12.19), the sales (10.79), the transport (12.76), and the
accommodation (12.04) industry.
An outlier analysis was conducted, and since none of the
countries are beyond the three standard deviations, we
decided not to exclude any of the countries from further
analysis. However, data were not available for the follow-
ing countries: FYR Macedonia, Greece, and Belgium,
which were therefore excluded from the analysis.
Step 3: K-means cluster analysis of RFID usage in EU
countries in selected industry sectors in 2014
Table 2 presents the clusters of the selected EU countries
grouped based on RFID usage in 2014, using the K-means
algorithm. We have chosen to use a three-cluster analysis
since the analyses with more clusters generate the clusters
with a very small number of countries (e.g. one cluster
consists of only one country with the overall highest level
of RFID usage in selected industry sectors).
Countries, which are grouped into cluster A, are:
Austria, Slovenia, Croatia, and Malta, and they have the
highest average usage of RFID in the information and com-
munication sector (ICT). Good result for the RFID usage in
Slovenia and Croatia is somewhat surprising, taking into
account that both are post-transition countries with the
higher corporate digital divide.
45
However, the high level
of RFID usage could be the result of the cross-border coop-
eration among the software companies from Croatia,
Slovenia, and Austria, with examples like Technology
Park Ljubljana.
Countries which are grouped into cluster B are: Cyprus,
the Czech Republic, Estonia, France, Hungary, Iceland,
Ireland, Italy, Latvia, Lithuania, the Netherlands, Norway,
Poland, Romania, Slovakia, Slovenia, Spain, and the
United Kingdom. Those countries have the lowest level
of RFID usage across all of the industries. Most of the
countries in this group are post-transition countries
(Estonia, Latvia, Lithuania, Poland, Romania, Slovakia,
and Slovenia), which have lower usage of digital technol-
ogies than more developed EU countries.
46
However, other
countries are more developed, like France, Iceland, Ireland,
Italy, the Netherlands, Norway, Poland, Spain, and the
United Kingdom, indicating that the RFID technology is
not yet common used in the business settings, and there are
number of barriers toward its usage, even in more developed
environments.
2,12
Cluster C contains countries with the highest level of
RFID usage, especially in the energy sector, construction,
and sales. Those countries are Denmark, Finland, Ger-
many, Luxembourg, Portugal, and Sweden. They mainly
come from the central and the northern part of the Europe,
with the exception of Portugal. This result is in line with the
highly developed economies of these countries, which are
also the leaders in the innovation, according to the Innova-
tion European Scoreboard indicators.
46
An average ratio of RFID usage of different clusters
(2014) is shown in Figure 3. It can be concluded that RFID
is used the least in the construction industry (8%—cluster
A, 5%—cluster B, and 9%—cluster C). Two industries
with the highest level of RFID usage are electricity and
other energies (19%—cluster A, 15%—cluster B, and
29%—cluster C) and ICT sector (37%—cluster A,
Table 1. Descriptive statistics of RFID usage according to industry of enterprises.
NMinimum Maximum Mean Standard deviation
Manufacturing 27 5 26 12.19 5.643
Electricity, gas, steam, air conditioning, and water supply 28 8 36 18.39 7.450
Construction 29 1 16 6.45 3.562
Wholesale and retail trade; repair of motor vehicles and motorcycles 29 4 23 10.79 4.887
Transport 29 5 21 12.76 4.673
Accommodation 28 1 34 12.04 7.126
Information and communication 28 12 46 22.14 7.920
Source: Authors’ calculation based on Eurostat data (2016a).
RFID: radio frequency identification.
Table 2. European countries according to cluster membership.
a
Countries in cluster A Countries in cluster B Countries in cluster C
Austria, Slovenia,
Croatia, and Malta
Cyprus, Czech Republic, Estonia, France, Hungary, Iceland, Ireland, Italy,
Latvia, Lithuania, the Netherlands, Norway, Poland, Romania, Slovakia,
Slovenia, Spain, and the United Kingdom
Denmark, Finland, Germany,
Luxembourg, Portugal, and
Sweden
Source: Authors’ calculation based on Eurostat data (2016a).
a
Due to missing data, the following countries have been omitted from the analysis: FYR Macedonia, Greece, and Belgium.
Bach et al. 5

18%—cluster B, and 23%—cluster C). The results for
these two industries are in line with their high level
of innovativeness.
47
Enterprises from selected EU countries in cluster A used
RFID the most in the following industries: accommodation
(25%) and ICT (37%). In addition, RFID usage by enter-
prises is also high in energy sector (19%), manufacturing
(17%), and transport industry (16%). However, the enter-
prises from selected EU countries in cluster A used RFID
the least in construction industry (8%). Enterprises from
selected EU countries in cluster B used RFID the most in
the following industries: energy (15%) and ICT (18%). The
lowest usage of RFID by enterprises in cluster B is in
construction industry (5%), just like in cluster A. However,
the enterprises from selected EU countries in cluster B used
RFID the least in all industries. In cluster C, RFID usage is
the highest in the energy sector (29%) and ICT (23%). Like
in the clusters A and B, the lowest usage of RFID by enter-
prises in cluster C is in construction industry (9%).
Step 4: Kruskal–Wallis test of the differences in
competitiveness among clusters as the result of the
step 3
In order to investigate whether there is a relation between
the level of competitiveness and RFID usage, we have
compared the countries from the clusters according to the
competitiveness indices. The Kolmogorov–Smirnov test
did not indicate the normality of the distribution of the
competitiveness indices, and therefore, the nonparametric
Kruskal–Wallis test was used in order to test the statistical
significance of the differences.
The average values of indices of technological readiness
are the highest in cluster C, in which countries whose enter-
prises use RFID the most can be found (Table 3). Countries
from clusters A and B have lower average values of indices
measuring the technological readiness, as well as the lower
value of RFID usage. The Kruskal–Wallis analysis
revealed that differences are statistically significant for
four competitiveness indices—the 9th pillar: technological
readiness (5%level); technological adoption (5%level);
availability of the latest technologies (10%level); and
firm-level technology absorption (5%level).
The average values of business sophistication are the
highest in cluster C, with one exception, that is, the average
value for local supplier quantity, which is the highest in
cluster A (Table 4). Countries from clusters A and B have
lower average values of competitiveness indices as well as
lower values of RFID usage (with the exception of one
index) (Table 5). The Kruskal–Wallis test revealed that
differences are statistically significant for four competitive-
ness indices: local supplier quality (10%level), state of
cluster development (10%level), nature of competitive
advantage (5%level), and willingness to delegate author-
ity (10%level).
The average values of innovation indices and global
measure of competitiveness are again the highest in cluster
C. Countries from clusters A and B have lower average
values of competitiveness indices as well as lower values
of RFID usage. The Kruskal–Wallis test revealed that dif-
ferences are statistically significant for almost all of the
analyzed competitiveness indices, except the 12.05 Gov’t
procurement of advanced tech products and 12.07 Patent
Cooperation Treaty (PCT) patents.
Discussion
In order to investigate the relationship between the compe-
titiveness indices and the level of RFID usage according to
the EU countries, the following procedure was used. First,
using the Kruskal–Wallis test, we investigated that the dif-
ferences in competitiveness between countries belonging to
clusters are statistically different. Second, we identified the
best and the worse clusters according to the average values
of competitiveness indices per clusters.
Table 6 summarizes the results of the Kruskal–Wallis
test for the year 2014. The analysis reveals that the average
index values of competitiveness are statistically significant
for almost all indices regarding 12th pillar: innovativeness
and overall competitiveness, mostly with the 5%level.
These results indicate that the relationship between RFID
usage and innovation is stronger than the relationship with
9th pillar: technological readiness and 11th pillar: busi-
ness sophistication. Such findings could be the result of the
fact that the RFID is the disruptive technology which is
more related to the innovativeness than the regular devel-
opment of technology and doing business.
48
Table 7 presents the clusters with the highest and the
lowest average values of competitiveness indices. In most
of the cases, the average competitiveness indices were the
highest in cluster C and the lowest in cluster A in most of
the cases (Table 6). These results had been expected, since
cluster C includes some of the most developed countries of
Figure 3. Average ratio of companies using RFID in different
industries according to clusters, 2014. Source: Authors’ calcula-
tion based on Eurostat data (2016a). RFID: radio frequency
identification.
6International Journal of Engineering Business Management

Europe, and previous research also indicated that the over-
all usage of the ICTs is related to the competitiveness.
49
Most of these are countries of Northern Europe (Den-
mark, Finland, Germany, Luxembourg, and Sweden),
with the exception of Portugal. Cluster A includes the
neighboring countries of Central and Southeast Europe
(Austria, Slovenia, and Croatia) and Malta, among which
only Austria could be considered as the highly developed
country. Cluster B contains the EU countries from dif-
ferent regions (Cyprus, the Czech Republic, Estonia,
France, Hungary, Iceland, Ireland, Italy, Latvia, Lithua-
nia, Malta, the Netherlands, Norway, Poland, Romania,
Slovakia, Slovenia, Spain, and the United Kingdom). The
countries from this sector are of various levels of eco-
nomic development, membership of the EU, and their
geographical position.
Therefore, the average values of competitiveness
indices according to clusters are the highest for the clusters
with countries that are leading in the usage of RFID, as one
of the most relevant innovations nowdays.
26
Previous
research indicates that the characteristics of new technolo-
gies challenges to the adoption of RFID are not just tech-
nological issues, but political and societal issues, as well as
regarding privacy, health, and environment.
50
Table 3. Competitiveness of cluster members according to the 9th pillar: technological readiness and Kruskall–Wallis test.
Competitiveness indicators
Cluster
Total
Kruskal–
Wallis w
2
AB C
Average
(Standard)
Average
(Standard)
Average
(Standard)
9th Pillar: technological readiness 5.32 (0.53) 5.28 (0.64) 5.99 (0.34) 5.45 (0.38) 6.229**
A. Technological adoption 5.11 (0.42) 5.20 (0.49) 5.67 (0.15) 5.30 (0.47) 7.320**
9.01 Availability of latest technologies, 1–7 5.60 (0.40) 5.65 (0.63) 6.26 (0.27) 5.78 (0.59) 5.910*
9.02 Firm-level technology absorption, 1–7 5.08 (0.48) 5.19 (0.62) 5.77 (0.12) 5.31 (0.57) 6.473**
9.03 FDI and technology transfer, 1–7 4.64 (0.51) 4.77 (0.66) 4.97 (0.38) 4.80 (0.58) 1.756
B. ICT use 5.53 (0.65) 5.36 (0.92) 6.31 (0.66) 5.61 (0.89) 5.384
9.04 Individuals using Internet (%) 71.96 (6.10) 76.26 (13.85) 85.97 (12.29) 77.84 (13.15) 2.537
9.05 Fixed broadband Internet subscriptions/100
pop
26.47 (4.65) 26.36 (7.90) 33.12 (5.48) 27.94 (7.37) 3.355
9.06 Int’l Internet bandwidth (kb/s per user) 369.01 (558.30) 148.19 (116.73) 1254.20 (2544.69) 437.40 (1247.02) 4.299
9.07 Mobile broadband subscriptions/100 pop 58.97 (12.38) 59.12 (20.57) 80.00 (34.06) 63.91 (24.19) 2.175
Source: Authors’ calculation based on Eurostat data (2016a) and World Economic Forum (2015).
ICT: information and communication technology, FDI: Foreign Direct Investment.
*Statistically significant at 10%.
**Statistically significant at 5%.
Table 4. Competitiveness levels of cluster members according to the 11th pillar: business sophistication and its composite measures
and Kruskal–Wallis test.
Competitiveness indicators
Cluster
Total
Kruskal–
Wallis w
2
ABC
Average
(Standard)
Average
(Standard)
Average
(Standard)
11th Pillar: business sophistication 4.53 (0.65) 4.56 (0.57) 5.18 (0.48) 4.70 (0.60) 4.299
11.01 Local supplier quantity, 1–7 5.13 (0.36) 4.75 (0.41) 4.92 (0.65) 4.85 (0.47) 2.405
11.02 Local supplier quality, 1–7 5.07 (0.66) 5.06 (0.40) 5.50 (0.30) 5.16 (0.45) 4.877*
11.03 State of cluster development, 1–7 4.03 (0.73) 4.18 (0.72) 4.86 (0.52) 4.31 (0.72) 5.140*
11.04 Nature of competitive advantage, 1–7 4.50 (1.00) 4.43 (1.02) 5.62 (0.92) 4.71 (1.08) 6.320**
11.07 Production process sophistication, 1–7 4.66 (0.98) 4.82 (0.86) 5.63 (0.65) 4.98 (0.88) 4.558
11.09 Willingness to delegate authority, 1–7 3.87 (0.56) 4.30 (0.90) 5.03 (0.83) 4.40 (0.90) 4.701*
11.06 Control of international distribution, 1–7 4.35 (0.49) 4.29 (0.46) 4.56 (0.45) 4.36 (0.46) 1.379
11.08 Extent of marketing, 1–7 4.67 (0.66) 4.83 (0.60) 5.29 (0.54) 4.91 (0.61) 3.392
11.05 Value chain breadth, 1–7 4.56 (0.65) 4.35 (0.57) 5.03 (0.48) 4.54 (0.60) 3.918
Source: Authors’ calculation based on Eurostat data (2016a) and World Economic Forum (2015).
*Statistically significant at 10%.
**Statistically significant at 5%.
Bach et al. 7

Conclusion
Development and usage of new information technologies,
such as RFID, provide new possibilities for enterprises,
which helps them to successfully compete in the global
market. EU countries are aiming to increase their
competitiveness by the usage of ICTs, under the Informa-
tion Society Agenda,
51
which reinforces its radio spectrum
policy. The contributions of this study are as follows: (1)
analysis of trends in RFID usage in the selected EU coun-
tries in 2009, 2011, and 2014; (2) cluster analysis using
K-means of the selected countries according to RFID usage
Table 5. Competitiveness levels of cluster members according to the 12th pillar: innovation and its composite measures and overall
competitiveness indices (innovation and general); Kruskal–Wallis test.
Competitiveness indicators
Cluster
Total
Kruskal–
Wallis w
2
AB C
Average
(Standard)
Average
(Standard)
Average
(Standard)
12th Pillar: innovation 3.80 (0.72) 4.05 (0.71) 5.03 (0.58) 4.24 (0.80) 8.350**
Innovation and sophistication factors 4.17 (0.69) 4.30 (0.63) 5.11 (0.51) 4.47 (0.69) 7.447**
Global competitiveness index 4.57 (0.43) 4.70 (0.42) 5.23 (0.46) 4.80 (0.36) 7.376**
12.01 Capacity for innovation, 1–7 3.99 (0.76) 4.31 (0.68) 5.22 (0.47) 4.47 (0.77) 9.252**
12.02 Quality of scientific research institutions, 1–7 4.36 (0.50) 4.84 (0.59) 5.60 (0.46) 4.94 (0.67) 8.005**
12.03 Company spending on R&D, 1–7 3.61 (0.80) 3.75 (0.78) 4.83 (0.74) 3.98 (0.88) 6.879**
12.04 University–industry collaboration in R&D, 1–7 3.92 (0.54) 4.38 (0.63) 5.24 (0.50) 4.51 (0.72) 9.320***
12.05 Gov’t procurement of advanced tech products, 1–7 3.39 (0.66) 3.48 (0.47) 3.95 (0.44) 3.57 (0.52) 4.095
12.06 Availability of scientists and engineers, 1–7 4.40 (0.54) 4.36 (0.48) 5.01 (0.69) 4.52 (0.58) 4.800*
12.07 PCT patents, applications/million pop. 56.52 (74.50) 71.96 (82.57) 158.19 (101.83) 89.49 (91.29) 4.336
Source: Authors’ calculation based on Eurostat data (2016a) and World Economic Forum (2015).
R&D: research and development.
*Statistically significant at 10%.
**Statistically significant at 5%.
***Statistically significant at 1%.
Table 6. Statistical significance for differences in competitiveness of clusters, 2014.
a
Technological readiness Business sophistication
Innovativeness and
overall competitiveness
9th Pillar: technological
readiness
5% 11th Pillar: business sophistication Ø 12th Pillar: innovation 5%
A. Technological adoption 5% 11.01 Local supplier quantity, 1–7 Ø Innovation and sophistication
factors
5%
9.01 Availability of latest
technologies, 1–7
5% 11.02 Local supplier quality, 1–7 10% Global competitiveness index 5%
9.02 Firm-level technology
absorption, 1–7
5% 11.03 State of cluster development, 1–7 10% 12.01 Capacity for innovation, 1–7 5%
9.03 FDI and technology
transfer, 1–7
Ø 11.04 Nature of competitive advantage, 1–7 5% 12.02 Quality of scientific research
institutions, 1–7
5%
B. ICT use Ø 11.07 Production process sophistication, 1–7 Ø 12.03 Company spending on R&D,
1–7
5%
9.04 Individuals using Internet
(%)
Ø 11.09 Willingness to delegate authority, 1–7 10% 12.04 University–industry
collaboration in R&D, 1–7
1%
9.05 Fixed broadband Internet
subscriptions/100 pop
Ø 11.06 Control of international distribution, 1–7 Ø 12.05 Gov’t procurement of
advanced tech products, 1–7
Ø
9.06 Int’l Internet bandwidth
(kb/s per user)
Ø 11.08 Extent of marketing, 1–7 Ø 12.06 Availability of scientists and
engineers, 1–7
10%
9.07 Mobile broadband
subscriptions/100 pop.
Ø 11.05 Value chain breadth, 1–7 Ø 12.07 PCT patents, applications/
million pop.
Ø
Source: Author’s research based on Eurostat data (2016a) and World Economic Forum (2015).
ICT: information and communication technology.
a
1%, 5%, and 10% indicate the level of statistical significance for the difference between average values of innovativeness indices per clusters. Ø indicates
that the difference was not statistically significant.
8International Journal of Engineering Business Management

in enterprises in selected industry sectors; and 3) analysis of
the relationship between RFID usage in enterprises in
selected industry sectors and the level of countries’
competitiveness.
The trend analysis revealed that RFID usage is increas-
ing in EU countries, but not at an equal speed. When RFID
usage in the period from 2009 to 2011 is compared to the
usage in the period from 2011 to 2014, improvements are
visible, but still a rather low percentage of enterprises in
EU countries uses RFID technology. The cluster analysis
revealed that countries in Europe can be partitioned into
homogenous groups regarding RFID usage among enter-
prises in different industry sectors. The Kruskal–Wallis
test revealed that countries that are in clusters with the
highest level of RFID usage also have a higher level of
competitiveness, measured using the global competitive-
ness index and indicators from its pillars—the 9th pillar:
technological readiness; the 11th pillar: business sophis-
tication; and the 12th pillar: innovation. The results on the
relation between competitiveness and RFID are in line
with the numerous previous researches on impact of ICT
to the competitiveness.
46,52
The findings of this research could be considered as a
valuable indicator for the future policy makers of strategies,
such as the Digital Agenda for Europe. The results indicate
that the level of RFID usage is still rather low. Only those
countries that are well advanced in the usage of RFID
technology will be able to use its positive impacts on the
effectiveness of business processes, while other countries
will be lagging behind. Results indicate that the most
advanced countries in the usage of RFID technology are
mostly countries from Central and Northern Europe (Den-
mark, Finland, Germany, Luxembourg, Portugal, and Swe-
den). Those countries are already among the technological
leaders in Europe, which probably reinforces their success
in the usage of RFID technology. Other countries still have
the chance to catch up, but more efforts should be invested
in promoting the radio spectrum policy. That approach
could support the expansion of the information society and
reduce the enterprise digital divide among EU countries.
Limitations of this research refer to data collection and
analyses, which were made for only 3 years, that is, 2009,
2011, and 2014. Also, in our work, we have focused only
on the EU countries, and future research should be
oriented toward the detailed analysis of RFID usage
among enterprises worldwide. Such research will proba-
bly reveal a very low level of RFID usage in the least
developed countries, which could additionally reinforce
the enterprise digital divide in their enterprises.
45
Data
analysis on the level of the enterprises would also be
beneficial for future analysis, since it could provide a
deeper outlook into the benefits of the RFID usage, as
well as incentives and obstacles toward its utilization.
Such analysis could be conducted using well-known the-
oretical frameworks of the technology adoption, such as
the technology adoption model or the technology-
organization-environment framework.
53
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with
respect to the research, authorship, and/or publication of this
article.
Table 7. Clusters with the highest and the lowest average values of competitiveness indices, 2014.
Technological readiness Business sophistication
Innovativeness and
overall competitiveness
9th Pillar: technological
readiness
C/B 11th Pillar: business sophistication C/A 12th Pillar: innovation C/A
A. Technological adoption C/A 11.01 Local supplier quantity, 1–7 A/B Innovation and sophistication
factors
C/A
9.01 Availability of latest
technologies, 1–7
C/B 11.02 Local supplier quality, 1–7 C/B Global competitiveness index C/A
9.02 Firm-level technology
absorption, 1–7
C/A 11.03 State of cluster development, 1–7 C/A 12.01 Capacity for innovation, 1-7 C/A
9.03 FDI and technology
transfer, 1–7
C/A 11.04 Nature of competitive advantage, 1–7 C/B 12.02 Quality of scientific
research institutions, 1–7
C/A
B. ICT use C/B 11.07 Production process sophistication, 1–7 C/A 12.03 Company spending on R&D,
1–7
C/A
9.04 Individuals using Internet
(%)
C/A 11.09 Willingness to delegate authority, 1–7 C/A 12.04 University–industry
collaboration in R&D, 1–7
C/A
9.05 Fixed broadband Internet
subscriptions/100 pop
C/B 11.06 Control of international distribution, 1–7 C/B 12.05 Gov’t procurement of
advanced tech products, 1–7
C/A
9.06 Int’l Internet bandwidth
(kb/s per user)
C/B 11.08 Extent of marketing, 1–7 C/A 12.06 Availability of scientists and
engineers, 1–7
C/B
9.07 Mobile broadband
subscriptions/100 pop
C/A 11.05 Value chain breadth, 1–7 C/B 12.07 PCT patents, applications/
million pop
C/A
Source: Author’s research based on Eurostat data (2016a) and World Economic Forum (2015).
ICT: information and communication technology; R&D: research and development.
Bach et al. 9

Funding
The author(s) disclosed receipt of the following financial support
for the research, authorship, and/or publication of this article: This
work has been partially funded by the Innovative Technologies
Centre S.A., Greece.
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