CASE Network Studies and Analyses 394 - Differentiation of Innovation Behavior of Manufacturing Firms in the New Member States. Cluster Analysis on Firm-Level Data

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This paper was produced in the framework of MICRO-DYN (www.micro-dyn.eu ), an
international economic research project focusing on the competitiveness of firms, regions
and industries in the knowledge-based economy. The project is funded by the EU Sixth
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The publication was financed from an institutional grant extended by Rabobank Polska S.A.
English proofreading by Paulina Szyrmer.
Key words: Innovation patterns of firms; Strategy of innovation, Innovation behaviour,
Innovation sources; Taxonomies of innovative firms, EU New Member States
JEL codes: L25, O31, O32, 033
© CASE – Center for Social and Economic Research, Warsaw, 2009
Graphic Design: Agnieszka Natalia Bury
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CASE Network Studies & Analyses No.394- Differentiation of Innovation Behavior of Manuf…
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Contents
Abstract...................................................................................................................................5
1. Introduction ......................................................................................................................6
2. Background ......................................................................................................................7
3. The Heritage of a Command Economy ..........................................................................9
4. Data source and enterprise sample..............................................................................12
5. Methodology employed to explore innovation patterns.............................................14
6. Aggregate factors description ........................................................................................15
7. Innovation patterns of firms in the NMS ........................................................................16
Conclusions..........................................................................................................................21
Bibliography .........................................................................................................................23
Appendix...............................................................................................................................26

Ewa Balcerowicz is a co-founder and the Chairwoman of the Supervisory Council of CASE
– Center for Social and Economic Research; from 1 July 2004 to 30 June 2008 she served
as President of the CASE Management Board. She has a PhD (1988) and Master’s degree
(1977) from the Warsaw School of Economics. Her research and publications focus on: the
SME sector, the environment for the development of the private sector, the banking sector
and insolvency systems, barriers of entry and exit in the transition economies of CEEC, and,
most recently, innovation economics.
Marek Pęczkowski is a lecturer at the Faculty of Economic Sciences at the University of
Warsaw. He specializes in business process modelling, multivariate data analysis, data
mining and econometrics. He has worked in numerous international research projects
involving statistical databases and statistical computing.
Anna Wziątek – Kubiak is a professor of economics and head of the Department of
Macroeconomics and Economic Policy at the Institute of Economics in the Polish Academy
of Sciences, a lecturer at the Dąbrowa Górnicza Business School and a scholar at CASE –
Center of Social and Economic Research. She has participated in and coordinated numerous
research projects focusing on international economics, including international trade and
competitiveness and innovations. She has authored and co-authored numerous articles and
books published by Springer, Palgrave and Edward Edgar.
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5
Abstract
This paper investigates the differences in innovation behaviour, i.e. differences in innovation
sources and innovation effects, among manufacturing firms in three NMS: the Czech
Republic, Hungary and Poland. It is based on a survey of firms operating in four
manufacturing industries: food and beverages, automotive, pharmaceuticals and electronics.
The paper takes into account: innovation inputs in enterprises, cooperation among firms in
R&D activities, the benefits of cooperation with business partners and innovation effects
(innovation outputs and international competitiveness of firms’ products and technology) in
the three countries. After employing cluster analysis, five types of innovation patterns were
detected. The paper characterises and compares these innovation patterns, highlighting
differences and similarities. The paper shows that external knowledge plays an important
role in innovation activities in NMS firms. The ability to explore cooperation with business
partners and the benefits of using external knowledge are determined by in-house innovation
activities, notably R&D intensity.

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1. Introduction
One of the main issues of economic growth and competitiveness in the New Member States
of the EU (NMS) is their innovativeness. As widely proved by economic research,
innovations stimulate the economic growth of countries and thus enable the NMS to catch up
with developed market economies. The NMS inherited an anti-innovation bias from the
command economy system. However, in response to the introduction of market institutions
and market rules in the 1990s, firms active in these countries faced increased competition
and had to modify their innovation behaviour.
In terms of innovations and economic performance, firms in the NMS are heterogeneous.
This raises the issue of differences in innovation patterns1 among firms, i.e. differences in
innovation sources and innovation effects. These countries were isolated from the world
economy for many years. During the transition period, new economic networks among firms
developed rapidly. Thus, the question emerges of whether or not enterprises also benefited
from cooperation with business partners in this period. In other words, we would like to know
if they gained the ability to absorb domestic and international knowledge spillovers. This
leads to a question about the role of external sources of innovation versus internal ones. Last
but not least, the relationship between innovation patterns and international competitiveness
is also of interest.
This paper aims to answer the questions listed above. Its purpose is twofold. Firstly, to
examine differences in the innovation activities of firms in the three NMS: the Czech
Republic, Hungary and Poland, as well as their sources and effects. Secondly, it aims to
detect and characterize the innovation patterns of manufacturing sector firms in the three
countries and their relationship with economic performance.
The paper is divided into two parts. In the first part the background for our study and
specifics of the NMS are presented. First, the main theoretical approaches in explaining the
process of differentiation of sources and modes of innovation among firms are presented
(Section 2). We summarize the results of research on the role of external versus internal
factors of innovations. Next, in Section 3 specifics of the NMS compared to developing and
developed market economies is shown. The second part of the paper presents the results of
our own research on innovation activities run by manufacturing firms in the NMS. To our
1 Or innovation modes – we use these two terms interchangeably.

knowledge, no analyses on differences in the innovation activities of firms have been
undertaken for the NMS so far. This part begins with a brief presentation of data source and
an enterprise sample (Section 4). In Section 5 we discuss the methodology employed to
detect firms’ innovation patterns in the NMS. Section 6 presents aggregate factors that
turned out to matter in clustering of enterprises by innovation indicators. The last section
presents and discusses innovation patterns of the NMS firms. It focuses on similarities and
differences between innovation patterns of firms and their relationship with economic
performance. Conclusions convene the paper.
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2. Background
For many years, most empirical studies on the diversity of innovation activities focused on
inter-industry variations. The studies neglected the heterogeneity of firms within industries
and intra-industry differences among firms in terms of innovation behaviour and strategy. At
the same time, the theoretical literature does provide some guidance in identifying sources of
inter-firm variation in innovation activities. It points out that the unevenness of the availability
of information, the various means used to innovate, the differences in expectations about the
return to R&D investment and other factors may lead to differences in innovation behaviour
and performance.
In theory, the differentiation of innovations within an industry is analysed from various points
of view. Two approaches play a crucial role2 in explaining the process of differentiating
sources and modes of innovation among firms: evolutionary theory and the theory of
endogenous growth. The former focuses on analyzing ways in which firms develop their
innovation process. The specific nature of the process of technological change of a firm and
the fact that innovation activities depend on the firm’s past history are at the heart of this
approach (Nelson and Winter 1982; Verspagen 2000). Heterogeneity in knowledge stocks
across firms plays a crucial role in the variation in enterprises’ innovation patterns. As a
result, firms differ significantly in terms of innovation capabilities: innovation inputs, activities,
scope, forms and partners of external cooperation, and innovation output. This also implies
2 There are many other approaches and theories which refer to the heterogeneity of firms’ innovation activities
within an industry. For example, the life cycle theory shows that at a given point in time, firms within a given
industry can be at different stages of development and innovativeness. This suggests the heterogeneity of their
innovation patterns. The strategic management literature shows that firms may intentionally seek to find different
innovation strategies from their competitors.

that for firms which did not accumulate knowledge in the past, the potential for creating
innovation and using it as a market-expansion factor is rather limited.
The excessive focus in evolutionary theory on the importance of internal resources as a
dominant factor of innovation created a tendency to neglect the contribution made by
external factors (i.e. knowledge linkages) and their role. The development of the theory of
endogenous growth and the endogenization of technological change into economic growth
resulted in the introduction of knowledge spillovers to the analysis on innovation (Grossman
and Helpman 1991, Rivera-Batis and Romer 1991). The non-rival character of knowledge
implies that firms may learn from other firms’ innovations. These are known as technological
(knowledge) externalities or spillovers. So a firm’s innovation capabilities depend on the
pool of knowledge it accumulated through internal efforts, on the pool of general knowledge
it has access to and its ability to use it. This means that apart from in-house capabilities
accumulated in the past, firms rely on external (both domestic and foreign) sources of
innovation when developing and introducing innovations. This approach also results in the
emergence of the notion of knowledge capital as a function of both the firm’s own R&D
investment and spillovers (Ornaghi 2006).
If knowledge is cumulative (in the sense that only leaders, that is creators of innovation, can
conduct innovative activities), then, as the theory of endogenous growth proves, an outsider
can also learn from the previously accumulated technology and acquire or imitate it. For
example, firms can enhance the quality of their product by learning from an innovation
introduced by competitors and by imitating it. In this way, firms can benefit from a positive
externality (a spillover). Outsiders can introduce a new product or simply upgrade the quality
of the existing one. However, they have to invest in this improvement as imitation also
requires some knowledge. So imitative activity is a type of learning activity, but the learning
of new knowledge is costly. This suggests that “in order to recognize, evaluate, negotiate and
finally adapt the technology potentially available from others,” (Dosi 1988, p. 1132) firms
require some in-house innovation capacity. A precondition for the endogenization of
knowledge spillovers is some accumulation of knowledge by the firm. The dual role of in-house
R&D activities as creator as well as adopter of innovations that spill over from external
actors has been recognised.
The discussion on sources of innovation inevitably leads to various taxonomies of firms in
terms of innovation capabilities, strategies, ways of creating innovation and modes of
innovation (Clausen and Verspagen, 2008; Srholec and Verspagen, 2008). Most of them are
based on two types of sources of innovation: internal and external, although in reality they
coexist. In many respects, the division of firms into cumulative and non-cumulative (Llerena,
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Oltra 2002) overlaps with the division of firms into those generating innovation and those
adopting innovation (Damanpour and Wischnevsky, 2006). Yet another criterion of
classification is by pioneering R&D and by imitating R&D that generates incremental
innovation. Other examples are taxonomies on STI (Science, Technology and Innovation)
and DUI (Doing, Using and Interacting) firms (Jensen et al. 2007). Although these
classifications differ in many respects, they have a dichotomous character as they distinguish
between two types of firms: leaders (creators of innovation) and outsiders. They reflect the
distinction between innovation and imitation and between innovators and imitators. The last
category is diversified. It covers incremental innovators, followers3 and traditionals4
(Avermaete et al., 2004).
The discussion on innovation sources, patterns of innovation, and their effects is very
relevant for the NMS. Both their heritage as centrally planned economies and the progress
they have made during the transition period, meaning the speed at which firms have adapted
and integrated into a highly competitive global economy, means that research on the
variation of innovation behaviour among firms in these countries provides an excellent test-case
of the sources of innovation and economic growth. This relates to the role of different
factors in innovation patterns and their results. It also shows the different faces of innovation
activities.
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3. The Heritage of a Command Economy
It seems reasonable to refer briefly to the command economy heritage for the innovativeness
of the countries of the Central Europe in their transition to a market economy (i.e. in the
entire decade of the 1990s) and the years preceding their EU membership. Firstly, although
under socialism, science and technology were very high on the list of government and
communist party priorities (Gomulka 1990, Chapter 7), the focus of research was on the
areas of science which did not require market validation.5 Secondly, for systemic reasons,
enterprises did not create demand for research from the universities, while the latter did not
deliver research results that served the market expansion of firms. There was no demand for
and no supply of research results that could have enabled producers to innovate. Numerous
3 They spend up to 1% of their annual sales on R&D
4 They do not perform R&D activities themselves; however they introduce new or substantially modified product or
processes.
5 The term used by Arogyaswamy and Koziol (2005), p. 456.

factors that formed the ‘constructional logic’ of the command economic system were in fact
anti-innovation (Balcerowicz 1995, Chapter 6). Nearly all research was government-sponsored
and was mostly theoretical in nature with hardly any market implications. The
prolonged isolation of these countries from the world economy and the structure of incentives
discouraged not only innovation but also imitation (Winiecki 2002, p. 14). “The enterprise
managers avoided innovation as much as possible if new technology and associated
organization arrangements affected the existing productive capacity (...) and they preferred
investment in new capacities, using the same (often already obsolete) technology, to
technological modernization” (Winiecki 2002, p. 13). The closed economies blocked
international linkages that impact on innovation, including knowledge spillovers. The
incentives characteristic of the command economic system resulted not only in low
competitiveness and technological obsolescence, but most of all in an anti-innovation bias
(Winiecki 2002). These countries and their firms did not accumulate innovation resources
due to their in-house innovation activities or international knowledge spillovers. The anti-innovation
bias of managers and employees and the resistance to privatisation in some
industries at the start and early years of transition made the enhancement of innovation quite
difficult. However, in terms of human capital, enterprises had a much greater potential to
innovate6 than most firms in developing countries.
During the transition period, the three countries that are of interest to this paper were
characterised by:
• A peripheral position with respect to global technology-intensive manufacturing
production; the structure of production was not conducive to innovation activities and the
quality of goods was very low;
• Low share of R&D and low share of business R&D spending in GNP;
• Low level of knowledge linkages between R&D organizations and firms as well as
among firms; inherited poor innovation capabilities of domestic firms accompanied by
radical changes in cooperation among firms (so called “adverse shock to network
activity”, see Woodward and Wójcik, 2007) as a result of privatisation and bankruptcy of
many firms;
In the early 1990s, defensive restructuring was taking place in the enterprise sector and it
was based on shedding labour, reducing costs and scaling down or closing unprofitable
6 Since the Marxian theory of economic development stressed the key role of economic efficiency, the innovation
rate and ultimately productivity levels in the competition of centrally managed economies with capitalistic ones,
the countries of the Soviet bloc placed an extraordinary emphasis on technical education (for evidence see
Gomulka 1990, p. 94).
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plants. In later years, strategic restructuring based on investment and innovation was
increasingly common (Konings 2003).
The opening up of the transition economies resulted in an increase in the competitive
pressure of foreign products and firms on domestic products and firms and created potential
for international knowledge spillovers. Their main channels were foreign trade and foreign
direct investment.
Here we come across the problem of the ability of the transition (NMS) countries’ domestic
firms to absorb knowledge spillovers from external sources, both domestic and international.
Absorption is not less important than generating new knowledge, including creating radical
innovation. The term ‘ability to absorb’ covers not only the implementation of external
knowledge. It also contains improvements in the knowledge which is imported (copied), i.e.
its upgrading.
First of all, as the NMS are knowledge absorbers, learners rather than creators, the role of
international knowledge spillovers in their innovation activities should be greater than in the
case of the old EU member states. However, the effects of international knowledge
spillovers depend on many factors and these effects may be positive or negative7.
Research on the NMS underlines crucial role of international spillovers for their accumulation
of knowledge and growth. Analysing 17 OECD countries including CEECs (Central and
Eastern European countries) Bitzer et al. (2008) came to a conclusion that productivity effect
of spillovers through vertical backward linkages between multinationals and domestic firms in
CEECs is much higher than for other OECD countries. Leon-Ledesma (2005) basing on
analysis of 21 OECD countries in a long run shows that for the G7 group foreign knowledge
has a negative impact on competitiveness, while for less advanced ones countries it has a
strong positive impact. This impact is stronger the higher the degree of openness to FDI.
However, research results are varied depending on the period of analysis, the country, the
model introduced, and the types of spillovers. Empirical research on the period up till 1998
(Konings 2001; Zukowska-Gagelmann 2001) showed negative spillovers effects of FDI for
domestic firms, although Damijan et al. (2003) did not confirm it. However, research results
covering period since 1999 and long term analyses do not confirm earlier research results
They did find more positive effects of vertical knowledge spillovers for domestic firms rather
than horizontal spillovers was found (Terlak 2004; Gersl et al 2007; Hagemajer and Kolasa
2008; Kolasa 2007; Bijsterbosch and Kolasa 2009; Gorodnichenko et al 2007). Some
research referred to the role of foreign trade as a source of international knowledge
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7 In 1992-1997, in opposition to Ireland and Spain, FDI in Greece did not generate positive knowledge linkages
externalities.

spillovers. Hagemejer and Kolasa (2008) show that differences in ability to absorb foreign
knowledge through spillovers varies among types of firms in terms of internalization. Last but
not least the issue of indirect knowledge spillovers as a result of R&D conducted abroad was
raised. It turns out that the impact of foreign R&D on productivity of the Central and East
European countries was greater than that of domestic R&D (Chinkov 2006; Tomaszewicz &
Swieczewska, 2008 and 2007). This is in opposition to what has been detected in the EU-15
(Leon-Ledesma 2005).
Summing up, the potential for radical innovations in the NMS is limited. Both the
accumulation of knowledge and R&D intensity are low although differentiated among these
countries8. The number of enterprises in theses countries engaged in innovation activities (as
a share of all firms) also remains low9.
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4. Data source and enterprise sample
The data used in this paper was collected through a firm survey performed by an
international research team led by Richard Woodward (of CASE-Center for Social and
Economic Research) and within the European research project entitled “Changes in
Industrial Competitiveness as a Factor of Integration: Identifying the Challenges of the
Enlarged Single European Market”.10 The survey was aimed at investigating the networking
of firms in the three accession countries (the Czech Republic, Hungary and Poland) and
Spain, and its effect on competitiveness11. Fortunately we have found a substantial number
of questions included in the survey questionnaire as relevant to the analysis of innovation
processes. Altogether 41 innovation indicators were selected. We grouped them into four
sets by the dimensions of innovation activities: (1) innovation inputs, (2) innovation linkages,
(3) effects of cooperation with business partners reflecting that diffusion of external
knowledge is taking place, and (4) innovation outputs. As many academics argue that in the
catching up economies diffusion can be the most important part of innovation, we decided to
include not only the linkages but also their effects. We also chose four performance
8 For example, in Poland, the share of R&D in GNP is almost three times lower than in the Czech Republic and
two times lower than in Hungary. Although R&D intensity in the Czech Republic is close to the average for the
EU-27, it is still not high enough to catch up in terms of the accumulation of knowledge of firms.
9 For Poland and Hungary, it was two times lower than the EU-27 average. Only in the case of the Czech
Republic was this indicator close to the EU-27 average.
10 It was funded by the 5th Framework Programme of the European Community (Ref. HPSE-CT-2002-00148).
The project was led by Anna Wziątek-Kubiak. CASE-Center for Social and Economic Research, Warsaw led the
research consortium.
11 For the results of this specific analysis, see Woodward and Wójcik (2007).

indicators: these are self-assessments of the competitiveness of a company’s products and
technology separately on the domestic and on the international markets.
All respondents surveyed were managers responsible for day to day business processes.
The interviews were conducted in 2004 in Hungary and Poland and in early 2005 in the
Czech Republic. The data collected refers to 2003 and in some cases to the five year period
1998-2003. This was an interesting and important period in the three former “socialist”
countries: they were undertaking market reforms, shifting from defensive to strategic
restructuring, covering innovation activities and advancing preparations for formal accession
to the EU, which happened on May 1st, 2004. Obviously both processes influenced the
behaviour of the real sector, i.e. firms, entrepreneurs and investors.
Data was collected for 490 companies. After carefully examining the answers received to
questions relevant for researching the innovation patterns, we had to delete 132 firms from
the data base, due to missing individual data. As a result the sample shrunk by ¼ to 358
firms. The composition of the sample is presented in Table 1.
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Table 1. Enterprise sample composition
No of
firms
% of the
sample
Countries
1. Czech Republic 70 20
2. Hungary 111 31
3. Poland 177 49
Ownership
1. Domestic 244 68.2
2. Foreign 108 30.2
Industry
1. Food and beverages 160 45
2. Automotive 65 18
3. Electronic 109 30
4. Pharmaceutical 24 7
Total 358 100

Polish firms dominated the sample: they accounted for close to half of the enterprise
population surveyed. The majority (ca 70%) of firms was domestically owned; and domestic
ownership prevailed in each individual country, though to different extents (Poland was on
one extreme with an 81% share of domestic capital, while Hungary was on the other
extreme, with only a 54.1% share of domestic companies). All size classes of firms were
investigated, but medium-sized firms dominated the sample.
Four industries were studied in the survey: (1) Food and beverages (NACE Rev.1 – da15);
(2) Pharmaceuticals (NACE Rev.1 – dg244); (3) Electronics (NACE Rev. 1 – dl30); and (4)
Automotive Industry (NACE Rev.1 – dm34). Food and beverages firms were the most
numerous (45% of the sample), while pharmaceutical firms appeared the least (only 7%).
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5. Methodology employed to explore innovation patterns
In order to figure out the innovation patterns of firms, a cluster analysis was adopted. Given
the relatively large number of innovation indicators (41), we decided to use principal
component analysis (PCA) to measure the sources of innovation in firms. PCA allows us to
reduce a large number of indicators to a small number of composite variables (called
‘factors’) that synthesize the information contained in the original variables. Factors are
standardised variables containing the information common to the original variables. In this
way, we were able to consider as much available information as possible. PCA is based on
the idea that indicators which refer to the same issue are likely to be strongly correlated and
factors that are obtained are uncorrelated. PCA helps prevent including irrelevant variables
and reduces the risk that any single indicator dominates the outcome of the cluster analysis.
We assumed that if the correlation between factors and original variables is lower than 0.48,
the analysis is inappropriate.
In the next step, non-hierarchical cluster analysis was performed in order to group firms into
a number of clusters by innovation variables as homogenous as possible (small within cluster
variance) and at the same time as different as possible from each other (large between
clusters variance).
In the Appendix, there is a table which shows the results of factor analysis for the three NMS
(Table A3). It includes the loadings of the variables on selected factors after the so called
rotation. The loadings of the various indicators on the retained factors are correlation

coefficients between the indicators (the rows) and factors (columns) and provide the basis for
interpreting the different factors. These loadings are adjusted through rotation to maximize
the difference between them. We use varimax Kaizer’s normalized rotation that assumes that
the underlying factors are uncorrelated.
The first step of factor analysis led to statistically satisfactory results. Eleven factors jointly
explaining, in the case of the three countries firms, 54.5% of the total variance were selected.
In the second step we conducted a non-hierarchical cluster analysis based on the eleven
composite variables extracted in the factor analysis of the first step. Introducing hierarchical
agglomeration methods for a subset of objects and comparing results for the range of K min
≤ K ≤ K max (where K is between 2 and 7), we chose the optimal number of clusters. Using
hierarchical analysis and Ward’s minimal variance method, we chose five clusters that group
the enterprises into five categories in terms of innovation indicators. Based on the distance
from the centroids, we compared the variance within clusters and between clusters.
Centroids of clusters obtained in the hierarchical method were used as the initial centroids for
the K-means algorithm.
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6. Aggregate factors description
The factors yielded in the cluster analysis have been further aggregated and as a result we
have received eight so called aggregate factors. These are:
• In-house inputs and activities (aggregate factor 1),
• two types of cooperation in R&D: backward (2) and with research organizations (3),
as well as subcontracting of R&D activities (4),
• beneficial cooperation with business partners: in product (5) and process (6)
innovation,
• type of innovation (7): either product or process or both ones,
• innovation outputs (8).
The aggregate factor 1 which is called ‘in-house inputs and activities’ groups a multitude of
internal innovation (research) inputs and activities of firms that may contribute to their
absorptive capacity and the creation of innovation (Cohen and Levinthal, 1989). It includes
the following variables: R&D intensity (R&D expenditures as a portion of firm’s sales
revenues), human resources (share of R&D, IT staff, engineers and technicians in total

employment), human capital upgrading through training, R&D unit in a firm, and R&D
activities in respect to product and process development and others.
Three aggregate factors encompass various collaborative networks in R&D. They cover
backward linkages (aggregate factor 2) that focus on cooperation in R&D with raw material
suppliers and machinery and equipment suppliers, as well as cooperation with research
organization- foreign and domestic and independent scientists (factor 3). The subcontracting
of R&D activities aimed at product and process development and improvements (aggregate
factor 4) is also considered.
Cooperation in R&D activities of firms in NMS in the late 1990s and early 2000s were still a
new phenomenon (see Section 2). Gaining experience on how to effectively profit from
others in extracting knowledge had to take time to learn. This was most likely the reason why
the cooperation was less common and effective than in developed market economies at that
stage. For this reason, two types of aggregate factors were selected: beneficial cooperation
with business partners in product innovation and in process innovation. They constituted
factors 5 and 6.
Two types of innovation activities: product and process ones constitute factor 7.
The last aggregate factor considers the output of firm’s innovation activities in terms of new
products and production technology introduced. However this factor did not retain for the
Czech Republic, while it was retained for the other two states and the three countries
altogether.
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7. Innovation patterns of firms in the NMS
After detecting the clusters, we analyzed their features. The first step was to study the values
of the innovation indicators that were chosen in the course of the cluster analysis. The data is
presented in Table A1 in the Appendix. The second step was to compare the value of each
factor (i.e. composite variables) between the clusters. We used the following scores: from
‘lowest’, through ‘low’, ‘moderate’, ‘high’ to ‘highest’. The third and last step was to analyze
all the scores for each cluster and invent a name for each one based on its distinguishing
features.
This procedure has brought us to the finding that the following innovation patterns emerged
in NMS firms during the EU accession preparatory period: (1) low profile, (2) hunting for

product innovation in the market, (3) spillover absorbers in process innovation, (4) on the
science-based innovation path and (5) externally sourced firms (see Table 2).
The detected innovation patterns represent the different innovation behaviours of firms as
well as different innovation outputs. The economic performance of sets of firms employing
individual innovation patterns varies as well. Surprisingly, the ownership structure of firms
realising these patterns does not differ considerably. Differences in the branch structure of
these firms are much greater.
Low profile pattern
Very low in-house innovation resources and activities as well as little external cooperation in
R&D distinguish this innovation pattern from the others. These features, together with the
focus on process (rather than product) innovation, and the fact that a relatively large portion
of firms benefit from cooperation in the production process suggests that the diffusion of
external knowledge, notably to the production process of these firms, plays an important role
in innovations. It serves for the accumulation of knowledge, which is very low.
The low innovation potential and the limited innovation activities of this group accompany
the worst - among the five subsets of firms (grouped by types of innovation behaviour) -
innovation outputs and international competitiveness. The moderate competitiveness of their
products and production technology on the domestic market allows them to operate in the
niche of this market, possibly in its lower quality segment. The use of external knowledge in
the production process indicates that they are conscious of their low competitive position and
to improve or maintain it, they focus on the absorption of external innovation.
From a general perspective, it is very telling that the low profile pattern firms in the NMS
accounted for 29% of the entire population surveyed. Most of the firms (ca 64%) following
this pattern are in the food industry, 22% in electronics, 11% in the automotive industry and
only 3% in the pharmaceutical industry. Surprisingly, the ownership structure of this subset of
firms is similar to that in other clusters (specifically, foreign owned firms accounted for 28% of
the total number of low profile firms).
Hunting for product innovation in the market
This cluster encompasses firms that focus on the adaptation of innovations by acquiring
them mostly from research organizations. Their R&D intensity is the lowest among innovation
patterns. This is accompanied by an extremely high (60%) share of R&D and IT staff in total
employment and the dispersion of R&D activities among many fields. Most of the firms have
R&D and design units. This suggests that in-house R&D activities focus on searching for new
product innovations on the market and better R&D subcontractors. Most of the firms gain
17

benefits from linkages in different forms of product development. The widespread diffusion of
innovation through subcontracting R&D is a crucial source of their innovation.
The market orientation of these firms is revealed through their high level of innovation output.
The share of new products in sales and the share of sales attributed to new technology was
one of the highest. Surprisingly, the internationally competitive position of products and
production technology was strong in most of these firms. This innovation pattern was the
least frequently undertaken: only 7 firms were adopting it. Interestingly, all of them were from
the same branch: electronics. The ownership composition of the cluster is not specific; it is
similar as in the case of other clusters.
Firms on the science-based innovation path
Firms pursuing a science-based innovation path rank high in the R&D factor (R&D intensity
and share of firms that have an R&D department). They also rank highly in cooperation in
R&D with different types of partners, notably with research organizations (including foreign
ones and independent scientists) as well as with suppliers of raw materials and machinery.
Their ease in cooperating with many types of partners reflects their ability to absorb not only
tacit but also codified knowledge, as well as their ability to accumulate external knowledge.
The fact that they score highly on the R&D factor and on external R&D collaboration
suggests the complementary role of two types of sources of innovation rather than the “make
or buy decision” (Veugelers, 1997; Veugelers and Cassiman, 1999) model. They score highly
on organizational changes as an effect of cooperation. However, the share of firms that
recognize cooperation in innovation activities as beneficial is average. This either reflects
their consciousness of their knowledge distance from main competitors (they expect that they
can gain more from the cooperation) or that they are in the process of searching for partners
that can better serve their innovation activities. A high number of in-house innovation
activities and cooperation in R&D does not translate into high innovation output and
international competitiveness. Although they come close to the STI/DUI mode of learning and
innovation (Jensen et al., 2007), the international competitiveness of their products remains
moderate.
This innovation pattern is pursued by foodstuffs and electronic firms (75% of the cluster
population); the ownership structure of firms in this cluster does not differ significantly from
other clusters.
18

Externally sourced firms
This innovation pattern shares some features with the one that relies on hunting for product
innovation. The common feature of the two is their low R&D intensity and high share of R&D
and ICT staff, which accompany a relatively high use of outsourcing of innovation results.
However, in opposition to ‘hunters’, firms pursuing supplier orientation in innovation
behaviour cooperate in R&D with many partners, including both research organizations and
suppliers of raw materials and machinery. Their product rather than process innovation
orientation is confirmed by a high innovation output and widespread number of firms that
benefited from product-oriented cooperation. However their ability to collaborate with
different partners does not translate into a very high innovation output or the strong
international competitiveness of their products. A considerable portion of firm managers
recognized their products and technology as weakly competitive, while the share of firms that
recognized their product and technology as strongly competitive was average in comparison
with the entire population of firms.
The firms using this innovation pattern differ from others in respect to branch structure. The
share of foodstuffs and automotive firms accounted for 27%, while electronics accounted for
33%.
Spillover absorbers in process innovation
In this cluster, we have firms that are in the process of developing R&D potential and
learning and this serves the absorption of external knowledge. The surprisingly high growth
of R&D spending and R&D intensity did not translate into cooperation with research
organizations. This explains why a considerable number of firms use the outsourcing of R&D
results, which is a substitute for cooperation with research organizations. Their
consciousness of the weaknesses of process innovations (confirmed by their weak
international competitiveness in terms of technology in a large number of firms) leads them to
cooperate strongly in R&D with suppliers of machinery and equipment. They benefit from this
cooperation quite considerably. On the other hand, they are also conscious of the role of
product differentiation in competition, as 72% of firms introduced new products and, for 50%
firms, this product was new to the market. International product competitiveness was
moderate for as much as nearly 2/3 of firms but was weak for only 8%.
The branch structure of this subset of firms is differentiated. Out of the total number of firms,
43% were foodstuffs producers, 32% were electronic manufacturers, and 19% were
automotive producers.
19

20
Table 2. The three NMS: Firms’ innovation pattern characteristics
Innovation
patterns
Innovation
factors
Low profile Hunting for
product
innovation
in the
market
Spillovers
absorbers
in process
innovation
Science-based
innovation
path
Externally
sourced firms
In house inputs
and activities
Lowest High R&D
staff and
innovation
activities but
low R&D
intensity
High High Moderate
Backward
linkages
Low High (but
supplier of
materials)
Moderate Highest High
Cooperation with
research
organizations
Lowest High Low Highest High
Subcontracting Lowest Highest Moderate Low High
Beneficial
cooperation:
product
innovation
Lowest High Low Moderate Highest
Beneficial
cooperation:
process
innovation
Moderate Lowest Highest High Low
Types of
innovation
Process Product Product/
process
Product Product
Innovation output Lowest Highest High Moderate High
International
P-lowest
P- highest
P-moderate
P - high
competitiveness
T-lowest
T- highest
T-moderate
T - high
P – moderate
T – moderate
Domestic
competitiveness
P-lowest
T-lowest
P – high
T - moderate
P – highest
T- highest
P – low
T-moderate
P – moderate
T – high
Cluster
composition
29% of the
firm sample;
Food-64%
2%;
Electronic-
100%
35%;
Food-43%
18%;
Food-38%
16%;
Automotive-
34%
P-product, T- technology

21
Conclusions
Although most firms in the NMS are imitators, non-cumulative (using the Llerena and Oltra
definition (2002, p. 185) and follow Jensen et al. (2007)’s DUI rather than STI mode of
learning and innovating, they differ in terms of partners and forms of cooperation in
innovation activities and in their internal capacities to innovate. The differences in innovation
behaviour as well as differences in innovation output and economic performance gave us a
base from which we could detect five types of innovation patterns.
On the one hand, a considerable number of sample firms (29%) are low profile that is they
are typical imitators. Their low innovation inputs, outputs and cooperation in innovation
means their products suffer from the lowest competitiveness on the international market and
only modest competitiveness on the domestic market. Their domestic orientation, their ability
to operate in market niches and in lower quality segments of the market allow them to
survive.
On the other hand, there are three groups of firms which make extensive use of external
sources of innovation, cooperate in innovation with many partners and are therefore
beneficiaries of this cooperation. Despite these similarities, they represent three different
innovation patterns. They differ in innovation strategy in terms of their in-house innovation
capacities, its forms (human capital versus R&D intensity), their strategies for using external
sources of innovation (the partners and forms of cooperation they focus on), areas of
spillover absorption and economic performance.
The first group of firms, labelled ‘hunting for product innovation in the market,’ represent a
type of outsourcing-oriented group of firms which were not detected in incumbent EU
countries. Their high share of R&D and ICT staff results in high ability to explore the
outsourcing of R&D and surprisingly they have the highest international product
competitiveness out of the entire population of analysed firms. However, their low R&D
intensity suggests a limited understanding of the role of accumulation of knowledge in future
expansion.
The next two groups of firms share quite an extensive and beneficial use of external
knowledge and have moderate international competitiveness. They differ in terms of the
types of weaknesses of their production processes and innovation potential. They have
varied R&D intensities, different shares of R&D and ICT staff in employment and they focus
on a different type of innovation (product versus process).

The high share of R&D and ICT staff in ‘the externally sourced’ firms allows them for
cooperation in R&D activities with different partners. Their low R&D intensity is to some
degree substituted by beneficiary cooperation with research organizations. On the other
hand, although the high R&D intensity of the firms within the next innovation pattern,
‘spillover absorbers in process innovation,’ supports collaboration in R&D with different
partners, in opposition to the previous firms, their absorption of knowledge spillovers is high
mainly in process innovation.
A specific group of firms termed as being on the science-based path has been also detected.
They represent Jensen et al.’s DUI/STI mode of learning and innovation. However their
relatively high R&D intensity (but low share of R&D and IT staff) and broad cooperation in
R&D with all types of partners, including foreign research organizations, does not transfer
into high international competitiveness. Rather, it remains moderate for most of these firms.
Analyses show that it was ‘the hunting for product innovation in the market’ innovation
pattern that was branch and ownership specific. The other four innovation patterns were
employed by firms in different manufacturing branches and of different ownership.
To improve international competitiveness, various firms in the NMS introduce different
innovation strategies. In innovation activities of most (but Low profile) detected groups of
firms, cooperation plays an important role. Differences in the partners and in the form of
cooperation differentiate the patterns of innovation of these firms. On the other hand, the
competitiveness of firms whose R&D intensity is very low is much lower than those whose
R&D intensity is higher (or at least moderate). However, a comparison of innovation patterns
of NMS firms raises the question of the reasons for the moderate international
competitiveness of firms that have high R&D intensity and extensive use of cooperation with
different partners in innovation activities. Is it because R&D activities require a critical mass
before being capable of generating new technology and yielding economic results and
firms’ budgets in the NMS are too tight to meet it? Or should high R&D intensity also be
accompanied by a high share of R&D staff? Is it also possible that they operate in the
countries that have specific characteristics that may influence their capacity to transform
R&D investment into economic performance? The scope of analysis in this paper does not
allow us to answer these questions.
22

23
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26
Appendix
Table A1. Firms in the Three NMS: Description of innovation patterns by types of
innovation indicators
(% of cluster’s firms answering ‘yes’ except for factors where other measures apply)
Innovation
patterns
Innovation factors and
indicators
(1)
Low
profile
(2)
Hunting
for product
innovation
in the
market
(3)
Spillovers
absorbers
in process
innovation
(4)
Science-based
innovation
path
(5)
Externally
sourced
firms
All firms
I. In-house innovation inputs and activities
Innovation activities in-house
R&D or design unit in-house
8.6 57.1 51.6 58.7 62.7 42.2
Process development
35.7 71.4 91.9 74.6 71.2 65.6
and improvement
activities in house
Product development
and improvement
activities in-house
30.5 71.4 95.2 82.5 72.9 69.8
Gathering commercial
and technical information
in-house
11.4 57.1 69.4 54 54.2 45.9
HR upgrading
Management training
very important
36.2 28.6 37.9 61.9 59.3 45.0
Employees training very
important
22.9 28.6 29.8 39.7 54.2 33.5
Human resources
Employment share of
technicians and
engineers (%)
8.8 54.3 9.0 7.0 15.2 10.4
Employment share of
R&D and IT staff (%)
3.0 40.0 3.0 1.0 4.3 3.2
R&D Intensity
(R&D to sales revenues,
%)
0.13 0.01 0.78 0.82 0.24 0.49
II. Innovation linkages
Backward linkages and cooperation R&D units and scientists. R&D department cooperates with:
Suppliers of raw
10.5 42.9 46.8 93.7 49.2 44.7
materials
Suppliers of machinery 2.9 85.7 41.1 85.7 42.4 38.8
Independentt scientists 1.9 57.1 8.1 66.7 40.7 22.9
Domestic research
institutes
19.0 85.7 44.4 95.2 49.2 47.5
Foreign research
institutes
3.8 28.6 5.6 57.1 27.1 18.2
Subcontracting of R&D activities
Process development /
improvements
14.3 100 22.6 12.7 61.0 24.3
Product development
/improvements
11.4 100 14.5 23.8 79.7 25.7

27
Innovation
patterns
Innovation factors and
indicators
(1)
Low
profile
(2)
Hunting
for product
innovation
in the
market
(3)
Spillovers
absorbers
in process
innovation
(4)
Science-based
innovation
path
(5)
Externally
sourced
firms
All firms
Design 4.8 14.3 34.7 20.6 50.8 25.7
III. Benefits of cooperation with business partners influencing both product and process innovation
In improved access to
39 14.3 54 46 28.8 43.3
modern technology
In improvement in the
production process
38.1 14.3 62.9 47.6 42.4 48.6
In modernization of
equipment
44.8 42.9 68.5 46 27.1 50.3
In inventories and
management
33.3 26.6 34.7 55.6 55.9 31.3
In product quality 61.9 71.4 71 73 93.2 72.3
In design 33.3 71.4 61.3 39.7 78 52.2
In R&D activities 24.8 85.7 53.2 38.1 69.5 45.5
IV. Innovation outputs
Share of new products and new technology in a firm’s sales revenues
Sales revenue share of
products less than two
years old
22.4 55 32.9 32.2 47.6 32.6
Sales revenue share of
production from
manufacturing
technology less than two
years old
40.2 55.3 47.8 45.8 59.7 47.3
New products introduced in the last two years and
New in a firm 55.2 71.4 72.6 68.8 64.4 65.6
Being new for domestic
33.3 85.7 52.4 47.6 42.4 45.0
market

28
Table A2. The Three NMS: Product and technology competitiveness of firms by
innovation patterns
(% of cluster’s companies answering ‘yes’)
Innovation patterns 1 2 3 4 5 All
firms
Company’s products
are:
strongly competitive
29.5
57.1
70.2
46
50.8
50
moderately
competitive
61 42.9 29.8 49.2 47.5 45.5
Competitiveness of
company’s
products on the
domestic market
weakly competitive 9.5 0.0 0.0 4.8 1.7 3.9
0ur products are:
strongly competitive 27.6 57.1 29.8 31.7 30.5 30.2
moderately
50.5 28.6 62.1 55.6 54.2 55.6
competitive
Competitiveness of
company’s
products on the
world market
Company’s technology
is:
strongly competitive 27.6 28.6 57.3 44.4 55.9 45.5
moderately
competitive
60.0 71.4 38.7 49.2 40.7 47.8
Competitiveness of
company’s
production
technology on the
domestic market
Company’s technology
is:
strongly competitive 24.8 42.9 26,6 36.5 23.7 27.7
moderately
competitive
47.6 42.9 52.4 47.6 54.2 50.3
Competitiveness of
company’s
production
technology on the
world market
weakly competitive 27.6 14.3 21 15.9 22 22.1

29
Table A3. The Three NMS: Results of Factor Analysis
Factors
Variables
1 2 3 4 5 6 7 8 9 10 11
Beneficial
Cooperation (BC)
with business
partners in i improved
access to modern
technologies
0.72
BC in improving the
production process
0.71
BC in modernization
of production
equipment
0.91
R&D or design unit in-house
0.53
Process development
in-house
0.79
Product development
in-house
0.75
Applied research in-house
0.49
Design in-house 0.67
Gathering
0.64
commercial and
technical info in-house
R&D department
cooperates with raw
material suppliers
0.81
R&D department
cooperates with
machinery and
equipment suppliers
0.79
R&D department
cooperates with
independent
researchers
0.49
R&D department
cooperates with
domestic institutes
0.50
R&D department
cooperates with
foreign institutes
0.63
BC in inventory
management and
improvement
. 0.70
BC in product quality
improvements
0.66
BC in product
specification and
design
0.49
BC in R&D activities 0.48
Process development
subcontracted
0.76
Product development 0.72

30
Factors
Variables
1 2 3 4 5 6 7 8 9 10 11
subcontracted
Design subcontracted 0.62
Managerial training
very important
0.81
Employees training
very important
0.82
Employment share of
technicians and
engineers in 2003
0.82
Employment share of
R&D and IT staff in
2003
0.82
Share of sales
revenues from sales
of new products in
2003
0.65
Sales revenue share
of production from
manufacturing
technology less than
2 years old in 2003
0.61
ISO certificate
received
0.51
New products
introduced in a firm
0.67
New products sold
and being new for
domestic market
0.70
R&D intensity in 2003 0.70

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