Knowledge Networks as a Source of Knowledge Initiatives
and Innovation Activity in Small and Medium Enterprises
Regression Analysis for EU 27 Countries
Ján Papula, Jana Volná and Jaroslav Huľvej
Faculty of Management, Comenius University, Odbojárov 10, Bratislava, Slovak Republic
Keywords: Knowledge Networks, Intellectual Capital, Core Competences, Innovation Activity, Clusters, Small and
Medium Enterprises.
Abstract: Building-up the knowledge society through human capital and innovation activities, particularly generated
through SMEs are generally the driving force of economic development, are developing options for future
competitiveness in the form of new knowledge, and are increasing the efficiency of the economy and its
ability to act. Since countries like Finland, Germany, Denmark and Sweden reach highest innovation
performance among all EU countries, there are countries like Latvia, Lithuania, Romania, Bulgaria, Poland
or Slovakia which drag behind other European countries and rank among the countries with the weakest
innovative performance. The aim of this paper is to identify the enablers of innovation in European SMEs
by using the concept of intellectual capital. Through statistical analysis we have investigated how
knowledge networks, which can be considered as the source of knowledge initiatives in SMEs, contribute to
their innovation activities. According to conclusions of our analysis, creating knowledge network, which
secure knowledge circulation and spilling over partners consisting of universities, state or local governments
and SMEs, will increase knowledge base of the economy of a country, i.e. will grow the innovation activity
of enterprises, improve the quality of human resources, research and technology, which are considered as
key factors of European competitiveness.
Innovation has become a major driver for economic
growth through the creation, use, and diffusion of
knowledge (OECD, 2002). As drafted in figure 1,
countries like Finland, Germany, Denmark and
Sweden reach highest innovation performance
among all EU countries.
Figure 1: EU Member States’ Innovation Performance
(European Commission, 2011).
On the other side, there are countries like Latvia,
Lithuania, Romania, Bulgaria, Poland or Slovakia
which drag behind other European countries and
rank among the countries with the weakest
innovative performance.
It is apparent, that states with lowest innovation
performance are all post-communist countries
entering the EU after 2004, of which the main
competitive advantage is the existing comparative
competitive advantage of low cost (low wages, low
taxes). In terms of the global economy, these
strategies are not further sustainable for mentioned
countries in the future. The growing competition of
countries having even cheaper labor quickly
devalues these temporary competitive advantages.
Based on the above, it is therefore clear that
mentioned countries must start focusing on value
added, knowledge-based resource advantages
instead of advantages originated from low cost. The
resource-based advantages are represented in the
knowledge base of the economy, specifically
growing innovation potential of enterprises, the
quality of human resources, research and
Papula J., Volná J. and Hul’vej J..
Knowledge Networks as a Source of Knowledge Initiatives and Innovation Activity in Small and Medium Enterprises - Regression Analysis for EU 27
DOI: 10.5220/0004548703890396
In Proceedings of the International Conference on Knowledge Discovery and Information Retrieval and the International Conference on Knowledge
Management and Information Sharing (KMIS-2013), pages 389-396
ISBN: 978-989-8565-75-4
2013 SCITEPRESS (Science and Technology Publications, Lda.)
technology, which are considered as key factors of
European competitiveness.
In order to increase the innovation activity in the
lowest ranked countries according to their
innovation performance (Pilková et al., 2012), the
focus has to be put on identification of the enablers
of the creation, use, and diffusion of knowledge
especially within SMEs. One possibility to provide
the identification of these enablers is to use the
concept of intellectual capital of an organization
identifying and quantifying the knowledge, skills,
relationships, business processes, innovation and
other components of intangible assets in the
organization which together aim to build and
strengthen the organization's competitive advantage
and also which aim to activate and enhance their
innovation potential. The outcome of effective
management of intellectual capital in organizations
is their increased innovation activity through the
creation of new products and services with high
added value for the customers.
The business sector, especially by SMEs, is
generally considered to be the innovation holder. To
fulfill this task requires professionally trained,
educated and creative human resources. The priority
therefore has to be put on creating innovative
companies with creative human capital and effective
internal and external communication, which are able
to add value for customer by using their knowledge
based resources (intellectual capital).
2.1 Importance of Knowledge
In companies based on knowledge, managing human
resources concentrates on increasing the so-called
organization intelligence and developing potential of
workers by means such as learning, participation,
co-operation and initiative. To know is an
advantage, to learn is necessity. This has always
been acknowledged. But these attributes are
gradually becoming the main comparative advantage
in a knowledge company now and they are the basis
for creating wealth.
Knowledge management is a term, which has
been currently appearing more and more often in
relation with the ambition of businesses to succeed
in the challenging competitive environment.
Knowledge management can be generally
understood as an effort to make know-how available
in an organization to those who need it, to where it is
needed, at the right time and in a form in which it is
needed in order to increase human and organization
performance. The main activities of knowledge
management are:
Acquiring knowledge and skills in the
Processing knowledge and skills within
Sharing knowledge and skills within organization;
Enhancement of knowledge and skills within
2.2 Intellectual Capital as Value
Adding Element of Knowledge
In contrast to first understandings of knowledge
management which had been focused on knowledge
distribution among creative individuals in the
company, later evolution of knowledge management
understands the employees (human capital of the
company) in the context of other elements of
intellectual capital (structural and relational capital)
and the knowledge management is understood as
management of intellectual capital of the particular
company (figure 2). The idea is, that in reality it is
not possible to separate employees from the
company's internal and external relations. Individual
items of knowledge are always oriented towards
something outside the person and therefore the
object of knowledge management has been
broadened to all parts of intellectual capital
(Mouritsen and Larsen, 2005).
The aim is to justify the interaction of skills and
knowledge of employees among each other, with
technologies and processes as well as with
customers, resp. with external environment of a
Intellectual capital, which incorporates skills and
knowledge at all levels of an organization, has
become the most important economic resource and
is replacing financial and physical capitals as the
most important source in the new economy.
Knowledge management including knowledge-based
activities, which build any of the components of
intellectual capital in the company, are nowadays
considered as the driving motor of sustainable
competitive advantage of an organization.
Figure 2: Main Activities of Knowledge Management.
Effective management of intellectual capital requires
the ability to choose among all skills and knowledge
those ones, which contribute to creation of key
processes and activities of organization.
Organizations often miss these valuable knowledge
and skills, which bring innovative potential to them
in relation to dynamics of external environment.
3.1 Definition and Characteristics
of Knowledge Networks
As written earlier, knowledge management is the
concept of modern management aimed at obtaining,
processing, distribution and multiplication of skills.
One possibility how small and medium companies
could use methods and tools of knowledge
management is creating knowledge networks.
Knowledge networking is the process by which
knowledge is transferred through collaboration,
coopfreration, and long-term network arrangements
(OECD, 2002). Knowledge networks usually engage
in three types of activities (Creech and Ramji, 2004):
• Collaborative research and information exchange:
the systematic investigation of the target issue or
problem, conducted jointly by two or more
members of the network, or by an individual
member with significant consultation with other
• Engaging with stakeholders: moving the research
into policy and action, through improved
communications and interaction with those who
are in a position to put the research to use.
• Network management: setting up and running the
operating structure necessary to build the
relationships among the participants in order to
strengthen the research, communications and
engagement processes of individual members and
of the network as a whole.
In support of these objectives it is needed to
create and develop a culture of entrepreneurs who
will not be afraid to engage in knowledge initiatives.
Small and medium enterprises cannot consider
knowledge sharing as the act of giving off their own
valuable specific know-how to competition.
Participation in the knowledge network enables
knowledge sharing that helps all businesses in the
area and allows small and medium enterprises to
jointly build competitiveness to foreign and
multinational corporations.
Theory and practice reveal that the interactions
between different agents involved in the innovation
process is important when examining the
characteristics of successful innovation (Morgan,
1996). Companies are no longer self sufficient for
the creation, development and commercial
exploitation of their knowledge base and,
consequently, seek inter organizational networks in
order to succeed in their respective technological
fields (Pena, 2002).
There are several major benefits from the
involvement of the enterprise in the knowledge
Strengthening innovation, by faster and more
efficient generating of creative ideas;
Reducing the risk of failure by the interaction with
network partners;
Accelerating innovation and lower costs of the
innovation process by knowledge and capacity
sharing at the network level;
Improving the efficiency of the mutual learning
and sharing of good practices;
Strengthening the trust and reputation outside the
network as well as between members of the
3.2 Core Competencies of a Company
as the Determinant Factor
in Decisions of What to Share via
Knowledge Networks
In order to survive and, what is more challenging, to
enhance competitive advantage, firms must possess
a knowledge base and capabilities which add value
to the firm; resources which are inimitable, no
substitutable and scarce (Pena, 2002). As early as in
1990, the authors C. K. Prahald and G. Hamel in
their article “The Core Competence of the
Corporation” developed the concept of Core
Competence of an organization. Key competencies
are only those skills that meet these following
criteria (Ireland et al., 2009):
1. They are valuable, so they contribute to value
creation for customers by exploiting new
opportunities or neutralizing threats.
2. They must be rare, so they are held by few if any
3. It must be difficult to imitate them. They are
difficult to re-create because intangible resources
or their specific contribution to the capability
cannot be easily identified.
4. They should be no substitutable. No resources /
capabilities should exist that can complete the
tasks and provide the same value to customers.
If today a firm is basing its competitive
advantage on one single product innovation or on
the use of other material or on the purchase of the
new technology, it will be quickly realized and
imitate by others, especially if the change is
effective. Therefore now it is important to prepare
competitive advantages that are hard to detect and
hard to imitate (Papulova, 2012).
According to this concept, the key capabilities -
or core competencies of the company are the main
source of its competitive advantage and they allow
the organization to create a new level of products
and services. To create core competencies in a
company, the organization must possess abilities
which can be used to create something valuable for
the customer and which other organizations do not
have. At the same time it has to be difficult to
imitate and unable to substitute. Knowledge and
company resources, which form valuable, unique,
not imitable as well as irrecoverable abilities are
those, which should not be therefore managed
through knowledge networks. For all other
knowledge – or components of intellectual capital of
the company, knowledge networks may be
An integrated knowledge management approach
should mix together firm internal core competencies
with inter organizational extensions to absorb and
transfer knowledge beyond the boundaries of a firm
(Pena, 2002).
3.3 Clusters as a Form of Knowledge
A cluster can be characterized as a network of
interdependent firms, knowledge-producing
institutions (e.g., universities, research institutes, and
technology-providing firms), bridging institutions,
and customers, linked to each other in a value-
adding production chain (Roelandt et al.,, 1999). A
cluster is a form of network that occurs within a
geographic location, in which the proximity of firms
and institutions ensures certain forms of
commonality and increases the frequency and
impact of interactions (Porter, 1998). There exist
several research studies with evidence that actors in
clusters tend to be more innovative than those that
are not in clusters (Baptista, 2000).
4.1 Research Setting
The present paper examines how knowledge
networks, which can be considered as the source of
knowledge initiatives in SMEs, contribute to their
innovation activities. In this paper we employ
regression analysis to estimate the quantitative effect
of an indicator reflecting the level of existing and
operating knowledge networks in particular EU 27
countries as the independent variable upon the
indicator reflecting innovation activity of SMEs in
these countries as dependent variable.
4.2 Definition of Measures and Data
Data have been collected from two different sources,
using the Innovation Union Scoreboard 2011
(European Commission, 2011) as the source of
innovation activity of EU 27 countries data and Star
Clusters reports (European Commission, 2011) as
the source of cluster involvement in specific EU 27
4.2.1 Independent Variable
As an independent variable, we have been looking
for an indicator reflecting the level of knowledge
networks existing and operating in particular EU 27
countries. As a source for data collection we have
used Star Cluster reports (European Commission,
2011), which describe regional clusters in 30
European countries. According to Star Cluster
reports (European Commission, 2011), the amount
and quality of knowledge circulating and spilling
over between firms, located in a cluster, is
dependent upon 3 characteristics:
1. The cluster's size;
2. The degree to which it is specialized;
3. The extent to which the locality (the region) is
focused upon production in the relevant
industries comprising the cluster.
The European Cluster Observatory shows the
extent to which clusters have achieved this
specialized critical mass by employing measures of
these three factors as described below, and assigning
each cluster 0, 1, 2 or 3 stars depending on how
many of the below criteria are met. For our analysis
we have used data about clusters, which received at
least one star (also 1, 2 or 3 stars) in this assessment.
Deeper insight into methodology of star
apportioning is described below (European
Commission, 2011):
1. A cluster has received the “size star”, if
employment reaches a sufficient share of total
European employment, it is more likely that
meaningful economic effects of clusters will be
present. The size measure shows whether a
cluster is in the top 10% of all clusters in Europe
within the same cluster category in terms of the
number of employees.
2. A cluster has received the “specialization star”, if
a region is more specialized in a specific cluster
category than the overall economy across all
regions. This is likely to be an indication that the
economic effects of the regional cluster have
been strong enough to attract related economic
activity from other regions to this location, and
that spillovers and linkages will be stronger. The
specialization measure compares the proportion
of employment in a cluster category in a region
over the total employment in the same region, to
the proportion of total European employment in
that cluster category over total European
employment. The measure needs to be at least 2
to receive a star.
3. A cluster has received the “focus star”, if a
cluster accounts for a larger share of a region's
overall employment, it is more likely that spill-
over effects and linkages will actually occur
instead of being drowned in the economic
interaction of other parts of the regional
economy. The focus measure shows the extent to
which the regional economy is focused upon the
industries comprising the cluster category and
relates employment in the cluster to total
employment in the region. The top 10% of
clusters, which account for the largest proportion
of their region's total employment, receive a star.
National statistical offices of participating EU
countries have been picked up as data sources for
Star cluster reports and data in these reports reflect
the situation in years 2001 – 2008, where in majority
of countries with reference year 2008. For the
purposes of our analysis, we have calculated
indicator consisting of Nr. of people employed in
clusters that had received at least one star through
Star Cluster assessment, divided with the Nr. of
inhabitants for every EU 27 country (later in text
marked as % CLU). The values of % CLU indicator
for EU 27 countries are shown in table 1. European
Countries are divided in the table into two groups,
first group consisting of 15 member countries in the
European Union prior to the accession of ten
candidate countries on 1st May, 2004 (labeled as
EU15) and countries entering into EU after 1st May,
2004 (labeled as EU new).
Table 1: Values of % CLU indicator for EU 27 countries
divided into EU 15 and EU new.
EU 15 EU new
Belgium BE 7.5 Bulgaria BG 10
Denmark DK 14.5 Czech Rep. CZ 9.9
Germany DE 8.1 Estonia EE 10.1
Ireland IE 8.6 Cyprus CY 9.3
Greece GR 8.3 Latvia LV 6.7
Spain ES 10.4 Lithuania LT 7.3
France FR 6.9 Hungary HU 7.7
Italy IT 10.6 Malta MT 11.7
Luxembourg LU 16 Poland PL 5.5
Netherlands NL 7.3 Romania RO 10.0
Austria AT 11.7 Slovenia SI 10.9
Portugal PT 10.7 Slovakia SK 7.9
Finland FI 8.5
Sweden SE
4.2.2 Dependent Variables
As a dependent variable, we have been looking for
an indicator reflecting the level of innovation
activity of SMEs in EU 27 countries. For the
purposes of our analysis we have decided to use the
Innovation Union Scoreboard 2011 (European
Commission, 2011) as a source. Specifically, we
have used two indicators from this report:
1. The indicator 3.1.1 - SMEs introducing product
or process innovations as % of SMEs (later in
text labeled as % PPI), since technological
innovation, as measured by the introduction of
new products (goods or services) and processes,
is a key ingredient to innovation in
manufacturing activities and higher shares of
technological innovators should reflect a higher
level of innovation activities (European
Commission, 2011).
2. The indicator 3.1.2 - SMEs introducing
marketing or organizational innovations as % of
SMEs (later in text labeled as % MOI), as the
indicator of non-technological innovation
activity of EU 27 countries.
Both indicators have used the statistics from
Eurostat from the reference year 2008 as the data
source. Values for these two indicators are shown in
table 2.
Table 2: Values of % PPI and % MOI indicator for EU 27
countries divided into EU 15 and EU new.
EU 15
EU new
State % PPI % MOI State % PPI % MOI
BE 44.0 44.1 BG 20.7 17.3
DK 37.6 40.0 CZ 34.9 45.9
DE 53.6 62.6 EE 43.9 34.1
IE 27.3 41.6 CY 42.2 47.3
GR 37.3 51.3 LV 17.2 14.0
ES 27.5 30.4 LT 21.9 21.4
FR 32.1 38.5 HU 16.8 20.5
IT 36.9 40.6 MT 25.9 25.6
LU 41.5 53.0 PL 17.6 18.7
28.6 RO 18.0 25.8
42.8 SI 31.0 39.4
43.8 SK 19.0 28.3
SE 40.6
UK 25.1
4.3 Data Analysis
Data in this paper are presented and analyzed
through descriptive statistics using histograms, box
and whisker plots, and statistics summaries such as
average, median, standard deviation, and kurtosis
and skewness. Then, normality tests have been
provided. After that, we have continued with
correlation and regression analysis and analysis of
variance, which have allowed us to analyze the
relationships among selected data. Data have been
executed in Microsoft Excel and Statgraphics Plus
software programs.
The main statistics summaries for all three variables,
%PPI, %MOI and %CLU are presented in table 3.
As seen in the table, all statistics summaries are very
similar for both, %PPI and %MOI. Figure 3 shows
the results of fitting a linear model to describe the
relationship between %MOI and %PPI. The
equation of the fitted model is:
%MOI = 4.76719 + 0.945769*%PPI (1)
Since the P-value is less than 0.01, there is a
statistically significant relationship between %MOI
and %PPI at the 99% confidence level. The R-
Squared statistic indicates that the model as fitted
explains 69.83% of the variability in %MOI. The
correlation coefficient equals 0.84, indicating a
moderately strong relationship between the
All these facts mean that the portion of SMEs
introducing product or process innovations in a
country is similar to the portion of SMEs
introducing marketing or organizational innovations.
Both, average and median is slightly higher for
%MOI reflecting the marketing or organizational
innovations than for %PPI reflecting product or
process innovations.
Table 3: Statistics summaries.
Indicator % PPI % MOI % CLU
Average 32.35 35.37 9.35
Median 32.09 36.73 8.60
St. Deviation 10.54 11.93 2.36
Kurtosis -1.03 -0.35 1.52
Skewness 0.03 0.14 1.07
Minimum 16.82 13.95 5.50
Maximum 53.61 62.63 16.00
Figure 3: Correlation Analysis for %MOI and %PPI.
As seen in table 3 describing main statistics
summaries, minimum and maximum value for
%CLU reflecting the level of knowledge networks
existing and operating in European Union countries
is 5.5 as minimum and 16 as maximum. It is very
interesting, that the portion of knowledge networks
is so similar for all EU27 countries and the data
range is only 10.5 points (where the maximum of
16% represents Luxembourg, which is a country
with the lowest population and higher percentage of
cross-border workers, thus it is feasible that the
%CLU indicator is affected by this fact). Figure 4
shows the box and whisker plot for %CLU data
divided into two groups, EU 15 and the rest
countries, entering the EU since 2004 (EU new).
Figure 4: Box and Whisker Plot for %CLU Data for EU
15 and EU New States.
To describe the relationship between the level of
knowledge networks operating in EU27 countries
and the innovation activity of SMEs in them,
regression analysis has been provided between these
variables. Figure 5 shows the results of fitting a
multiplicative model describing the relationship
between %CLU and %MOI. The equation of the
fitted model is:
%CLU = 3.66701*%MOI^0.259078 (2)
Figure 5: Relationship between %CLU and %MOI.
Since the P-value in the analysis of variance table is
less than 0.05, there is a statistically significant
relationship between %CLU and %MOI at the 95%
confidence level. The R-Squared statistic indicates
that the model as fitted explains 16.11% of the
variability in %CLU after transforming to a
logarithmic scale to linearize the model. The
correlation coefficient equals 0.40, indicating a
relationship between these variables.
Figure 6: Relationship between %CLU and %PPI.
Figure 6 shows the results of fitting a multiplicative
model to describe the relationship between %CLU
and %PPI. The equation of the fitted model is:
%CLU = 3.76108*%PPI^0.258029 (3)
Since the P-value is less than 0.10, there is a
statistically significant relationship between %CLU
and %PPI at the 90% confidence level. The R-
Squared statistic indicates that the model as fitted
explains 14.35% of the variability in %CLU after
transforming to a logarithmic scale to linearize the
model. The correlation coefficient equals 0.38,
indicating a relationship between the variables.
The activity of SMEs within EU27 countries in
knowledge networks or clusters is a clear
demonstration of their efforts to strengthen
competitiveness. At the country level, there is the
clear evidence of the relationship between the level
of existing and operating knowledge networks and
the innovation activity of SMEs in EU countries.
This should be a motivating factor for businesses
to seek and engage in knowledge networks or
clusters. This is important especially for small and
medium-sized enterprises, where the ability to
multiply and enhance knowledge by their own is
strictly limited. For new knowledge needed to
support innovation, they must also search in the
external environment. Maintaining the pace of
innovation requires to find the right partners to
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D 15
achieve the appropriate synergies through joint
action in the knowledge network, but also to focus
on correct settings in the internal environment.
Without adequate organizational culture, internal
processes and support of employee development
towards the search and usage of knowledge, the
involvement to the knowledge network could be
ineffective and without the desired results.
To ensure a sustainable competitive strategy,
there is necessary to activate the processes of
knowledge management by effective inclusion of
own knowledge-based resources (intellectual
capital) of company. Here occurs the importance of
intellectual capital management point of view to
secure the necessary enablers to support knowledge
management activities at the level of knowledge
networks. The concept of intellectual capital allows
managers to align resources and activities with
regard to the strategic objectives of the organization,
but also to measure and evaluate the activities
leading to the effective participation in knowledge
networks or clusters.
Regular monitoring and evaluation can help to
maintain activities and thus to promote a sustainable
innovation capability of enterprises. This is relevant
especially for countries that today do not achieve the
desired results in innovation activity (Latvia,
Lithuania, Romania, Bulgaria, Poland or Slovakia).
These countries should focus on comprehensive
management of activities, not just the obvious
process of knowledge management, but also at
building enablers consisting of the sources of
intellectual capital.
Limitations of our research: The research is
focused on analyzing the relationship between
engagement in knowledge networks represented by
clusters and innovation activity of companies,
especially small and medium enterprises. We didn't
analyze the level of activity within knowledge
networks or clusters, neither to analyze the structure
of intellectual capital with regard to the effective
usage of the possibilities of knowledge networks or
clusters. On these areas we plan to focus in our
future research.
This paper has been funded by project Vega
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