Plane Thermoelastic Waves in Infinite Half-Space Caused
FACTA UNIVERSITATIS
Series: Economics and Organization Vol. 11, N
o
2, 2014, pp. 133 - 147
Review paper
ANALYSIS OF SERBIAN INNOVATION POTENTIAL
IN THE PERIOD 2009-2012
UDC 330.341.1(497.11)”2009/2012”
Slobodan Cvetanovic
1
, Danijela Despotovic
2
, Vladimir Nedic
3
,
Vojislav Ilic
4
1
Faculty of Economics, University of Nis, Serbia
2
Faculty of Economics, University of Kragujevac, Serbia
3
Faculty of Phil. and Arts, University of Kragujevac, Serbia
4
Teacher Training Faculty, University of Belgrade, Serbia
Abstract. In this paper a review of significance of country’s innovation potential for its
economic growth and development is displayed first. Afterwards, positions and values
of the global innovation index for the top 25 most innovative economies, for Serbia and
for selected countries from its surroundings, for the period from 2009 to 2012 have
been displayed. In order to classify selected countries into two or more groups, based
on their similarity according to innovation performances, cluster analysis is conducted.
The relations between innovation inputs and innovation outputs have been studied on
the example of selected groups of countries (the group of European innovative leaders
and Serbia with neighboring countries) through the correlation analysis.
Key Words: innovation, innovation inputs, innovation outputs, innovation efficiency.
INTRODUCTORY NOTES
A larger share of new products, services and processes is one of the key assumptions
of generating economic growth and improving competitiveness of the country, regardless
of the level of its economic development. Growth in innovation potential, on one hand
and improving its competitiveness, on the other hand, is the long term requirement for
economic and social progress of all countries regardless of the level of economic
development (Cvetanovic, Mladenovic, Nikolic, 2011).
The score of the achieved level of innovation of countries is based on a larger number
of data. This study used data from the Global Innovation Index Report 2011, 2012.
Received April 22, 2014 / Accepted June 25, 2014
Corresponding author: Slobodan Cvetanovic
Faculty of Economics Niš, Trg kralja Aleksandra 11, 18000 Niš, Serbia
Tel: +381 18 528 642 E-mail: slobodan.cvetanovic@eknfak.ni.ac.rs
S. CVETANOVIC, D. DESPOTOVIC, V. NEDIC, V. ILIC 134
The objectives of this research are: a) an explication of the most important theoretical
basis on which the concept of innovation potential of the national economy rests,
b) review of the metrics of the innovative potential of the economy - Global Innovation
Index, c) an empirical analysis of the level and dynamics of the improvement of innovation
potential in the economy of Serbia in comparison to 25 most innovative economies in the
world and to its surrounding countries in 2009-2012 period.
For an explanation of the main pillars for the concept of innovation potential of the
economy, as well as to reflect on the Global Innovation Index metrics in addition to the
descriptive approach, graphics explication of the phenomena studied was used. On the
other hand, as dominant analytical tools for empirical analysis of the achieved levels and
dynamics to improve the innovative potential of selected countries, quantitative tools of
correlations and cluster analysis were used.
1. THEORETICAL FUNDAMETALS FOR CONCEPT OF INNOVATION POTENTIAL OF THE ECONOMY
The explication of the supporting theoretical pillars of the concept of the innovation
potential of the economy is not an easy task. It is our opinion that it is not possible to
understand the importance of innovation potential for the development of modern
economy properly without understanding the messages of three teachings that occupy a
significant place in the development of economic science and designing of economic
policies of advanced countries over the last twenty or more years. That being said, we
have in mind: a) the emergence of new theories or endogenous development, whose
holdings are represented by endogenous growth models of Paul Romer (Romer, 1986,
1987, 1990), b) recognition of the concept of national innovation system by Christopher
Freemen (Freeman, 1987) and c) learning about creating competitive advantage of
nations by Michael Porter (Porter, 1990). The unifying thread of these three teachings,
which is defined in the context of the title of this work, the highest possible analytical
importance has the position in which the improving innovation of the economy is at the
epicenter of explanation of the physiology of macroeconomic phenomena such as the
economic growth and the competitiveness of countries (Cvetanovic & Sredojevic, 2012).
Endogenous growth explanations emphasize the existence of positive correlation
between the dynamics of the improvement in innovation potential of the country and the
quality of the country's key macroeconomic performances. Also, it founds incentives for
innovation in the appropriate institutional arrangements as the innovators are not able to
realize the benefits of their results in an unfavorable institutional environment, which
inhibits the growth of its innovative potential (Jones, 1998).
Endogenous growth (Romer, 1994) theory has not yet become a full conceptual
approach to research key factors in the economic prosperity of individual countries. For
its creation and promotion we credit a number of economic theorists. Consciously risking
going into unjustified neglect of contribution of a large number of researchers to the
explanation of complex mechanisms for generating growth, in this paper, a new theory of
growth is associated with model presentation for the growth of the American Nobel
laureate Paul Romer from the period 1986 -1990 (Romer, 1986, 1987, 1990). In addition,
Romer's theoretical opus about the key drivers of economic growth is divided into two
parts; endogenous growth models based on externalities (Romer, 1986, 1987) and growth
models, the basis of which are research and development activities (Romer, 1990).
Analysis of Serbian Innovation Potential in the Period 2009-2012 135
Growth models based on externalities (Jones, 1995) start from the premise by which
innovation in the broadest context of the economy as a whole allow expression of
increasing returns, which is completely contrary to the assumption of perfect competition
(Romer, 1986). In the absence of perfect competition, knowledge cannot be perfectly
protected with patent or trade secret. The knowledge that each individual company
creates by "learning by doing" becomes instantly available and free to all interested
parties for their use. So the company can see that it "leaks" new knowledge, but it has
benefited from the knowledge that "leaks" to others. This means that at time t there is
same level of knowledge for all firms, i.e. the same for the whole economy. This level can
be represented by the equation: At = cKt b, for b> 0, where At is the level of technology
(innovation potential of the economy), Kt capital (physical) b the elasticity of At for the
change in Kt, and c is a constant. The equation At =cKt b indicates that the level of
technology (innovation potential size of the economy) depends on the accumulation of
capital at time t. Thus, the total stock of knowledge j is an increasing function of investment
and determined by the actions of economic agents, which makes a complex of technological
change endogenous. Since the companies are unaware of the production of knowledge, it is
always considered that the level of technology At as a given size and, at the same time, a
factor which can be used at no additional cost (Valdés, 1999).
Previous explicit model was not entirely satisfactory, primarily because of the
circumstances that the complex technology (innovation) in the treatment of it was accidental
result of the economic activities of the company (Sener & Sarıdogan, 2011). Specifically, in it
the companies maximize profits, investing in capital by process of learning by doing and
knowledge spillover effects (Cohen & Levinthal, 1990), increasing the general level of
knowledge regardless of the fact there is no explicit intention to do it. However, in real life,
the facts are that the new knowledge is in the minimum percentage the result of accidental
activities, while it is dominantly a result of work of companies who deal with innovation
activities in an organized way, while trying to realize monopoly rents (McElroy, 2003;
Teece, 2003). Thus, the implicit assumption by which new knowledge is available to everyone
for free, as well as the assumption according to which there is perfect competition; make the
largest structural defects of this model. Romer associated improvement in the innovation
potential of the national economy with an undeniable need for innovators to use commercial
valuation of their solutions in order to make profit (Romer, 1986).
Growth in Romer's model is based on research and development and driven by
innovation, and results from investment decisions of companies that maximize profits
(Romer, 1990, 1994). Romer recognizes that the technology is different from all other
goods, because it is uncompetitive in nature and only partially exclusive good. A good level
of competitiveness of any good is exclusively its technological feature. Competitively good
is used by one company or one person which understandably means it is not to be used by
anyone else. In contrast, non-competitive good is available to all without any restrictions.
Unlike competitive features, exclusivity of a good is a function of both technology and the
legal system. Good is considered exclusive, when its owner can prevent others from using
it. Conventional economic goods are distinguished by features of competitiveness and
exclusion. Public goods are uncompetitive and non-exclusive. Precisely because they are
non-exclusive, the supply of public goods cannot provide security to private individuals,
and they cannot be traded in the market. For the theory of growth there is an interesting
group of goods that are not competitive, but also partially exclusive, and technology is
such kind of good. There are three basic assumptions on which Romer build his model
S. CVETANOVIC, D. DESPOTOVIC, V. NEDIC, V. ILIC 136
based on the importance of research and development activities in 1990: a) technological
change (innovation) is a key determinant of economic growth, b) innovations are mainly
the result of deliberate actions taken by individuals to respond to market incentives and,
finally, innovations are by their characteristics different from other economic goods. These
three assumptions directly lead to the conclusion that the equilibrium is not possible in
conditions of perfect competition, but there must be a monopolistic competition. In fact, if
all inputs were paid according to marginal product, the company would have losses arising
from the additional expenses associated with previous investments in research and
development of new products or new processes. In the model of economic growth of
Roberta Solow this problem is abstracted due to the fact that the complex technological
change (innovation) is treated as an exogenous character variable (Solow, 1956). However,
this model is consistent with the premise on which technological change (innovation) is a
key determinant of economic growth and at the same time it is a non-competitive good.
However, the model is not consistent with the real fact that technology is the result of
planned and organized activities of economic actors to maximize cash benefits.
National innovation system comprises of a network of public and private institutions
whose activities and interactions determine the emergence, import, continuous improvement,
and the general diffusion of new technologies (Freeman & Soete, 1997). The concept
connects institutions and determinants of quality of innovation processes in the country
(Etzkowitz, et al., 1998). The attribute "national" includes many categories in which the
state has a certain impact. In short, the national innovation system is the totality of
relationships between organizations and relations involved in the production and diffusion
of scientific and technological knowledge (innovation) in the manufacturing process and
the society at large is the territory bounded by national borders (Freeman, 2002; Lundvall et
al., 2002). In the simplest form, the national innovation system model describes the mutual
relationship of the elements of which it is composed, the private sector, whose role is
reflected in the use of technologies developed as a result of its own research, market
winning of innovations, support of the country in the creation of new theoretical and
applied knowledge as well as the creation of infrastructure and institutional conditions
conducive to the development of innovation activities in private companies. In a word, the
national innovation system should be understood as form of an organization of economy
and society, which, in conditions of turbulent changes in the environment, ensures
sustainable development of the national economy (Peters, 2006).
The idea of the concept of national innovation systems in rudimentary form can be
found in the works of German economist Friedrich List (Peters, 2006). List identified a
number of significant determinants of investment such as industrial production,
institutions, import foreign technology, education and training. List's main concern was,
as some of the authors say, how Germany can overcome its economic backwardness in
relation to England, which was then the world's leading industrial nation, and how the
economy will catch up and surpass England (Freeman & Soete, 1997). As a reminder, the
paper argued for the protection of young industries and appropriateness of the policies
able to accelerate and facilitate the industrialization and economic growth. Most of these
policies were concerned with teachings about innovation and economic effects of their
specific application. The most important characteristic of this strategy was its devotion to
the proactive role of the state. List realized the importance of understanding the
interdependence of innovation and economic development, concluding that in order to
improve the innovation potential of Germany, the government should outline and implement a
Analysis of Serbian Innovation Potential in the Period 2009-2012 137
long-term policy support for the development of science, technology and industrial
production.
There are large differences in innovation potentials of the countries that have similar
production resources in the standard sense of the word, which again has to do with the
key performance of their national innovation systems (LeBel, 2008). Talking about
innovation in this light, there is a regard to the use and continuous improvement of
existing solutions, as well as the intense process of gaining new knowledge. In both
cases, primarily referring to the knowledge that exists within and outside the company,
but as far as this other form; we have in mind the knowledge that exists in the country in
which the company operates.
A number of authors believe that the concept of national innovation systems primarily
emphasizes the importance of tacit knowledge in generating technological innovations
(Simoneti, 2001). Otherwise, under the assumption that knowledge is codified explicitly
and unambiguously, the company could simply buy it like any other factor of production.
However, tacit knowledge means that the company must maintain numerous contacts
with other firms, as well as with a number of different organizations in order to gain
access to knowledge and especially to make it effective in use.
National innovation systems are formed under the influence of many different factors
for each of the analyzed countries, including its size, the availability of natural and
human resources, the characteristics of the historical development of public institutions
and the dominant forms of entrepreneurial activity (Figure 1). These factors determine to
a significant degree the level and dynamics of the innovative potential of improving the
national economy (Smith, 2010).
Fig. 1 National innovation system
Source: Modified according to (OECD, Managing National Systems of Innovation, 1999)
S. CVETANOVIC, D. DESPOTOVIC, V. NEDIC, V. ILIC 138
Michael Porter's key determination is that innovations initiate and support competition.
The main determinants of competitiveness of individual countries are:
a) conditions relating to the factors that determine the dynamics of production and
forms of manifestation profiling the competitive struggle in certain areas of business
(capital, level of technology, infrastructure, skilled workforce, available information, etc),
b) Conditions related to internal demand for goods and/or services of given production
areas, c) the presence of related competitive industries in the country and d) conditions in
the country that determine how the company is set up, organized and lead, as well as the
nature of domestic competition (Porter, 1990).
Fig. 2 Porter's diamond of national competitiveness
Source: Modified according to (Porter, 1990)
In the view of Porter, the success is achieved by those countries in which the process of
interaction of all the factors of national competitive advantage is most dynamic. Significant
improvement of innovation in the economy is not possible, if one of these determinants of
the diamond of national competitiveness does not make its full contribution.
2. METRICS OF GLOBAL INNOVATION INDEX
In this paper, innovativeness of the economy is quantified based on data from Global
Innovation Index (2011, 2012).
Global Innovation Index is based on two sub-indices: Innovation inputs and
Innovation outputs. Innovation inputs consist of five pillars that display elements, i.e.
potentials for innovative activities of national economy: (1) Institutions, (2) Human
capacity, (3) Infrastructure (4) Market sophistication, and (5) Business sophistication.
Innovation outputs consist of two pillars that show the actual results of innovation:
(6) Scientific outputs and (7) Creative outputs. Each pillar is divided into sub pillars and
each sub-pillar consists of individual indicators (see Figure 3).
Using the model shown in Figure 3, the country will be measured in accordance with
its Innovation inputs and outputs, which together determine the overall value of GII and
place the country on a ranking list made under the criteria of innovation.
Analysis of Serbian Innovation Potential in the Period 2009-2012 139
Fig. 3 Metrics of Global Innovation Index
Source: Modified according to (The Global Innovation Index, 2012)
Input parameters determine benefits of the environment in which economic actors
operate to create and effectively use various types of innovation in the economy. Outputs
are the results of the proof of innovation inputs: patents, trademarks, copyrights, creative
products, workers in the areas of knowledge-based services, the share of exports of high-
tech products in total exports, etc.
3. INNOVATION LEADERS, SERBIA AND NEIGHBORING COUNTRIES
Figure 4 shows place in the rankings according to the criteria of innovation in the
period 2009-2012 (top 25 most innovative economies in the world).
Figure 5 shows place in the rankings according to the criteria of innovation in the
period 2009-2012 (Serbia and selected countries in Europe).
There has been a major qualitative shift for Serbia in the criterion of Global Innovation
Index in 2012 compared to previous years. In fact, from 101st place in 2010 (and 92nd
place in 2009) Serbia was ranked 46
th
according to this criterion in 2012, surpassing
Greece, a long-time member of the European Union. However, even under the condition
that there is no doubt about the statistics incompatibility in the data on the basis of which
the Global Innovation Index is composed, the fact is that the surrounding countries,
Hungary, Slovenia, Bulgaria, Croatia, are significantly ahead of Serbia according to the
criterion of Global Innovation Index in 2012. From the countries bordering Serbia only
Bosnia and Herzegovina and Macedonia are behind it in the Global Innovation Index
(Cvetanovic, Despotovic, Nedic, 2012).
S. CVETANOVIC, D. DESPOTOVIC, V. NEDIC, V. ILIC 140
Fig. 4 Rankings of the top 25 most innovative economies in the world
Source: The diagram is based on the database from (The Global Innovation Index, 2011, 2012).
Fig 5. Rankings of Serbia and selected countries
Source: The diagram is based on the database from (The Global Innovation Index, 2011, 2012)
The question timely arises as to what extent innovation input size determines the
value of innovation output. Depending on the answer to such a question we can provide
useful information to policymakers in which direction it is most appropriate to work on
incentives and other government measures to improve innovation of the economy. In
order to get the answer to the question of dependencies in values that make innovation
inputs and innovation output components, we will use the statistical analysis of a very
well known, so called XY diagram. This is a common way to show the connection
(direction and degree of quantitative variation agreement) between two variables.
Figure 6 shows the scatter diagram of the relationship between the variables of
Innovation inputs and outputs.
Analysis of Serbian Innovation Potential in the Period 2009-2012 141
Fig. 6 Scatter diagram for the relationship of Innovation Input and Innovation Output
(data on a sample of 125 countries, in 2011)
Source: The diagram is based on the database from (The Global Innovation Index, 2011, 2012)
A graphical representation of data pairs of innovation inputs and innovation outputs
shows a strong correlation between the variations of the observed variables. Customizing the
linear form of interdependence and analysis of components in the specified model also
suggests previously stated, perceived visual statement. In fact, linear regression function has
the following form: y = - 2.678 + 0.766 X, with statistics of R
2
= 0.714 and R = 0.845.The
value of the coefficient of determination indicates the presence of 71.4% variation in variable
innovation output is explained by variations in innovation inputs, while the remaining
28.6% is a result of the influence of other factors not included in this model. Strong
correlation is confirmed by the correlation coefficient 0.845. Testing the hypothesis of linear
interdependence of variables over the corresponding regression coefficient obtains the value
of the test statistics at 17.527. With probability 0.05 level of significance of the test and the
test threshold at 1.9794, we also conclude that there is a statistically significant linear
correlation between the variables of innovation inputs and innovation outputs.
Figure 7 scatter diagram shows the relationship between the variables of Global
Innovation Index and Innovation Efficiency Index.
Fig. 7 Scatter diagram for the relationship of Global Innovation Index and Innovation
efficiency index (data on a sample of 125 countries, in 2011)
Source: The diagram is based on the database from (The Global Innovation Index, 2011, 2012)
A graphical representation of data pairs for the Global Innovation Index and
Innovation Efficiency Index shows a very weak correlation between the variations of the
S. CVETANOVIC, D. DESPOTOVIC, V. NEDIC, V. ILIC 142
observed variables. Customizing the linear form of interdependence and analysis of
components in the specified model also suggests previously stated, perceived visual
statement. In fact, linear regression function has the following form: y = 0.546 +0.004 X,
with statistics of R
2
= 0.102 and R = 0.319.The value of the coefficient of determination
indicates that only 10.2% of the variation in variable Innovation efficiency index is
explained by variations of the Global Innovation Index, while the remaining 89.8% is a
result of the influence of other factors not included in this model. Weak correlation is
confirmed by the correlation coefficient 0.319. Testing the hypothesis of linear
interdependence of variables through appropriate regression coefficient obtains value of the
test statistics at 3.237. With probability level of significance of the test at 0.05 and the test
threshold at 1.9794, we also conclude that there is a statistically significant linear correlation
between the variables Global Innovation Index and Innovation Efficiency Index.
4. CLUSTER ANALYSIS
By cluster analysis, the observed set of elements is divided into subsets, so that the
elements that are similar in some sense are grouped in the same cluster. In this case the
method used was agglomerative hierarchical clustering.
Figure 8 shows the dendrogram of the cluster analysis conducted between clusters for
which we used data for the Global Innovation Index, innovation index of efficiency, input
and output sub-index with the corresponding pillars of The Global Innovation Index
2012. X axis gives the diversity level between the countries analyzed.
In the process of grouping selected European innovation leaders according to the degree of
efficiency innovative bottom-up agglomerative hierarchical clustering method was used. In
the initial step, each country is treated as a separate cluster. Their merging in pairs of clusters
is based on the similarity in terms of the observed values of the variables which is the result of
all subsequent clustering iterations until all observed entities are consolidated within one
cluster. If we take diversity level of 600 as a possible cross-section in dendrogram, three
clusters of the observed countries are clearly identified. The largest group consists of 8
countries, or 53% of the total number of observed countries. The second group includes
Norway, Austria and France. The third group relates to Luxembourg and Ireland.
If we consider the world's innovation leader and take the cross section at diversity
level of 1250, it is possible to clearly identify two dominant clusters in the presented
dendrogram. A striking feature of the first cluster is that its two sub cluster elements are
created at a much higher level of diversity than is the case with countries that belong to
another cluster. The countries included in the cluster are characterized by a much higher
degree of variations in level of innovation effectiveness than is the case with countries
within the other cluster. Also, in comparison with clusters that are formed for European
leaders, the grouping for the world's leaders in clearly segregated clusters was achieved at
a much higher level of diversity, which suggests the expressive degree of variability in
the innovative effectiveness worldwide.
Figure 9 shows the dendrogram of the cluster analysis implemented for Serbia and
selected group of 11 European countries, the diversity is given on the Y axis. Diversity is
determined on the basis of data for GII, sub-indices of innovation inputs and innovation
outputs and the corresponding pillars.
Analysis of Serbian Innovation Potential in the Period 2009-2012 143
Fig. 8 The dendrogram of the cluster analysis conducted for the European and global
innovation leaders
Source: The diagram is based on the database from (The Global Innovation Index, 2012)
Fig. 9 Dendrogram of the cluster analysis implemented for Serbia and selected group of
countries
Source: The diagram is based on the database from (The Global Innovation Index, 2012)
Cluster analysis applied to Serbia and a selected group of countries follows a similar trend
for grouping as countries in the category of European innovation leaders. On the dendrogram
presented, it can be seen that from the innovative aspect of the degree of efficiency, at the first
Singapore
Ireland
Luxembourg
Hong Kong
Switzerland
Sweden
Denmark
Canada
Netherlands
USA
UK
Japan
Iceland
New Zealand
Germany
Korea
Finland
Israel
Norway
Australia
Hungary
France
Estonia
Austria
Belgium
0 500 1000 1500 2000 2500 3000
Dissimilarity
France
Austria
Norwey
Ireland
Luxembourg
Sweden
Switzerland
Finland
Iceland
Germany
Netherlands
Denmark
UK
0 500 1000 1500
Dissimilarity
S. CVETANOVIC, D. DESPOTOVIC, V. NEDIC, V. ILIC 144
level of grouping, Serbia is most similar to Greece and then to Croatia, Poland, Bulgaria and
Romania. On the other hand, there is the biggest difference compared to Hungary and
Slovenia. Overall, at the diversity level of 900 we can identify two clusters, i.e. Hungary and
Slovenia on one side against all other countries covered by the analysis.
5. COMPARATIVE ANALYSIS OF INNOVATION FOR SERBIA AND NEIGHBORING COUNTRIES
IN 2012
In Figure 10 in the given diagrams, we analyzed Serbia's position in relation to the
surrounding by GII and sub-indices of GII.
Fig. 10 Innovation Input Sub-Index, Innovation Output Sub-Index and Global
Innovation Index, Serbia and neighbors
In order to obtain a more realistic picture of the relationship between innovation
inputs and outputs we will investigate the correlation.
Graphical representation of data pairs of variables Innovation input and Innovation
output for the selected group of countries indicates a weak (negligible) correlation among
the variations of the observed variables.
Adaptation of the linear form of correlation and analysis of specified model
components also suggests previously stated, visually perceived statement. In fact, the
function of the linear regression has the following form: y = -14.3 +1.048 *x, with the
statistics R2 = 0.319 and R= 0.565. Value of determination coefficient shows that 31.9 %
of total variations of Innovation output variable is explained by the variations of
Innovation input variable, while the remaining 68.1% represents the result of the
influence of the other factors which are not included in this model. A weak correlation is
also confirmed by the correlation coefficient 0.565. Its value indicates the existence of
low grade, direct (straight line extending from the lower left to upper right corner of a
graph) linear correlation among the observed variables in countries included in the
sample. The slope of the line b1 = 1.048 indicates that the growth of Innovation input
variable for one unit of its measurement leads to growth of Innovation output for 1.048.
Testing of the hypothesis of linear independence of observed variables over the
Analysis of Serbian Innovation Potential in the Period 2009-2012 145
corresponding regression coefficient gave the value of the statistics test of 1.6788. With a
probability level of significance of the test 0.05 and test threshold 2.4469, it can also be
concluded that there is no statistically significant linear correlation between the observed
variables Innovation input and Innovation output.
Fig. 11 Scatter diagram for the connection between global innovation index and
innovation efficiency index (data on a sample of eight countries)
Fig. 12 Scatter diagram for the connection between global innovation index and
innovation efficiency (data on a sample of thirteen EU countries)
Graphical representation of data pairs of variables Innovation input and Innovation
output for the selected group of countries indicates a potentially significant correlation
among the variations of the observed variables. Adaptation of the linear form of
correlation and analysis of specified model components also suggests previously stated,
visually perceived statement. In fact, the function of the linear regression has the
following form: 15.49 + 0.557 *x, with the statistics R
2
= 0.473 and R= 0.7. Value of
determination coefficient shows that 47.3% of total variations of Innovation output
variable is explained by the variations of Innovation input variable, while the remaining
52.7% represents the result of the influence of the other factors which are not included in
this model. A potentially significant correlation is also confirmed by the correlation
coefficient 0.7. Its value indicates the existence of high grade, direct (straight line extending
S. CVETANOVIC, D. DESPOTOVIC, V. NEDIC, V. ILIC 146
from the lower left to upper right corner of a graph) linear correlation among the observed
variables in countries included in the sample. The slope of the line b1 = 0.557 indicates that
the growth of Innovation input variable for one unit of its measurement leads to growth of
Innovation output for 0.557. Testing of the hypothesis of linear independence of observed
variables over the corresponding regression coefficient gave the value of the statistics test
of 3.1474. With a probability level of significance of the test 0.05 and test threshold
2.201, it can also be concluded that there is statistically significant linear correlation
between the observed variables Innovation input and Innovation output. Thus, given the
values obtained with the proposed model, it can be concluded that the model is valid for
statistical inference, and implementation of correct predictions and projections of Y.
CONCLUSION
If observed world-wide, global innovation index data analysis shows significant difference
between the economies, even when they have similar general economic development. That
could be the consequence of countries' implementation of various distinctive strategies.
However, it is obvious that there is a significant correlation between innovation inputs and
innovation outputs, if they are observed globally, while this correlation cannot be identified in
the relation between global innovation index and innovation efficiency index (IFI).
Serbia and surrounding countries have innovation performance quality at a much lower
level compared to other EU countries. One of the reasons for delayed transition of Serbian
economy is its low innovativeness.
In considering the relationship between Innovation Input and Innovation Output Index
for Serbia and a select group of countries, it was found that there was no statistically
significant effect of innovation input on innovation results.
Considering the relationship between Innovation Input Index and Innovation Output Index
for reference European countries revealed a potentially significant direct linear correlation,
and statistically important impact (linear correlation) of inputs to innovation results.
Possible reason for this correlation disbalance within two observed groups of countries
is that GII metrics is primarily appointed to the countries with high-profile national
innovation system.
However, Serbia is the only country from the observed group of neighbouring countries
which has a very similar subindex of innovation inputs and outputs, and because of this is
on the first place in a group by innovation efficiency index. Unfortunately, it is our opinion
that this is an echo of innovation inputs from the time of Yugoslavia, and that, in order to
give recommendation and priorities for Serbian innovation system’s further development,
more serious focus on GII paremeters is necessary.
This requires further research after implementation of given metrics in following
period of time.
REFERENCES
1. Cvetanović, S. Mladenović, I. Nikolić, М. (2011). ―Theoretical basis of the concept of the innovative
capacity of economy‖ (in Serbian: Teorijske osnove koncepta inovacionog kapaciteta privrede),
Ekonomika, Niš, No 4, str. 14-23.
2. Cohen, W. M., & Levinthal, D. A. (1990). Absorptive capacity: a new perspective on learning and
innovation. Administrative science quarterly, 128-152.
Analysis of Serbian Innovation Potential in the Period 2009-2012 147
3. Cvetanović, S. Despotović, D. Nedić, V. (2012). ―Comparative analaysis of Serbian business
sophistication and neghboring countries‖, Industrija, No 4. pp. 89-106.
4. Cvetanović, S., Sredojević, D. (2012). ―The concept of national innovation systems and economic
competitiveness‖, (in Serbian: Koncept nacionalnog invoacionog sistema i konkurentnost privrede),
Ekonomske teme, Ekonomski fakultet, Niš, No 2. str. 167-185.
5. Etzkowitz, H., Webster, A., & Healey, P. (Eds.). (1998). Capitalizing knowledge: New intersections of
industry and academia. Suny Press.
6. Freeman, C. (2002). Continental, national and sub-national innovation systems—complementarity and
economic growth. Research policy, 31(2), 191-211.
7. Freeman, C. Soete, L. (1997). Economics of Industrial Innovation, Pinter, London; pp. 295-297.
8. Freeman, C. (1987). Technology Policy and Economic Performance: Lessons from Japan, Pinter,
London: p.4.
9. Jones, C. I. (1995). R & D-based models of economic growth. Journal of political Economy, 759-784.
10. Jones, C. (1998). Introduction to Economic Growth, Norton and Company Inc..
11. LeBel, P. (2008). The role of creative innovation in economic growth: Some international comparisons.
Journal of Asian Economics, 19(4), 334-347.
12. Lundvall, B., Dalum, B., Johnson, B., & Sloth Andersen, E. (2002). National Systems of Production,
Innovation and Competence Building. Research Policy, (31): 213-231.
13. McElroy, M. (2003). The new knowledge management: Complexity, learning, and sustainable
innovation. Routledge.
14. OECD, (1999). Managing National Systems of Innovation. Paris: OECD.
15. Peters, S. (2006). National Systems of Innovation, Creating High-Technology Industries, Palgrave,
Macmillan; pp. 18-24.
16. Porter, M. (1990). The Competitive Advantage of Nations, London, Macmilan.
17. Romer, P. (1990). ―Endogenous Technological Change―, Journal of Political Economy; 98 (5):78 -102.
18. Romer, P. (1987). ―Growth Based on Increasing Returns Due to Specialization‖, American Economic
Review, 77 (2): 56-63.
19. Romer, P. (1986). ―Increasing Returns and Long-Run Growth‖, Journal of Political Economy, 94 (5):
1002-1037.
20. Romer, P. M. (1994). The origins of endogenous growth. The Journal of Economic Perspectives, 8(1), 3-22.
21. Sener, S., Sarıdogan, E. (2011). The Effects Of Science-Technology-Innovation On Competitiveness And
Economic Growth. Procedia - Social and Behavioral Sciences, Volume 24, Pages 815-828
22. Simoneti, R. (2001). ―Governing European Technology and Innovation‖ in Tomson, G. (ed) The
Europian Economy, Sage Publacions, London.
23. Smith, D. (2010). Exploring Innovation, McGraw-Hill, p. 288.
24. Solow, R. (1956). ―A Contribution to the Theory of Economic Growth‖, Quarterly Journal of Economics;
70 (1): 65–94.
25. Teece, D. J. (2003). Capturing value from knowledge assets: the new economy, markets for know-how
and intangible assets. Essays on Technology Management and Policy, 47-75.
26. The Global Innovation Index. (2011). INSEAD.
27. The Global Innovation Index. (2012). INSEAD.
28. Valdés, B. (1999). Economic Growth: Theory, Empirics and Policy, Cheltenham, Edward Elgar.
ANALIZA INOVACIONOG POTENCIJALA SRBIJE U PERIODU
2009-2012. GODINE
U ovom radu prvo je prikazan pregled značaja inovacionog potencijala zemlje za njen
ekonomski rast i razvoj. Nakon toga su prikazane pozicije i vrednosti globalnog indeksa inovativnosti
za 25 najinovativnijih ekonomija sveta, za Srbiju i za odabrane zemlje iz njenog neposrednog
okruženja, za period od 2009 do 2012. U cilju klasifikacije odabranih zemalja u dve ili više grupa,
na osnovu njihove sličnosti prema inovacionim performansama, izvršena je klaster analiza. Odnosi
između inovacionih ulaza i inovacionih izlaza su prikazani na primeru odabranih grupa zemalja
(grupa evropskih inovativnih lidera sa jedne i Srbije i njenih susednih zemalja sa druge strane )
putem korelacione analize.
Ključne reči: inovativnost, inovacioni ulazi, unovacioni izlazi, inovaciona efikasnost.