clusters versus cluster initiatives, with focus on the ict sector in poland

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Clusters versus Cluster Initiatives, withFocus on the ICT Sector in PolandArkadiusz Michał Kowalskia & Andrzej Marcinkowskib

a World Economy Research Institute, Warsaw School of Economics,Warsaw, Polandb Institute of Social Sciences and Management of Technologies,Faculty of Organization and Management, Technical University ofLodz, Lodz, PolandPublished online: 22 Oct 2012.

To cite this article: Arkadiusz Michał Kowalski & Andrzej Marcinkowski (2014) Clusters versusCluster Initiatives, with Focus on the ICT Sector in Poland, European Planning Studies, 22:1, 20-45,DOI: 10.1080/09654313.2012.731040

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Clusters versus Cluster Initiatives, withFocus on the ICT Sector in Poland

ARKADIUSZ MICHAł KOWALSKI∗ & ANDRZEJ MARCINKOWSKI∗∗

∗World Economy Research Institute, Warsaw School of Economics, Warsaw, Poland, ∗∗Institute of Social

Sciences and Management of Technologies, Faculty of Organization and Management, Technical University

of Lodz, Lodz, Poland

(Received May 2012; accepted September 2012)

ABSTRACT The article focuses on the topic of clustering, which has become a popular concept, bothfrom the academic and political perspective, and as an efficient business model. The distinctionbetween clusters, understood as geographical concentrations of specific industries, and clusterinitiatives, understood as more formalized actions undertaken by regional actors, is proposed. Theprimary objective of this study is to verify if these two types of structures are overlapping eachother. This problem arises because the motivation for forming some cluster initiatives may bedifferent economic policy instruments rather than existing market potential of a specific regionaleconomy. The study finds that not all of Information and Communication Technologies (ICT)cluster initiatives in Poland represent real concentration of ICT-related divisions included instatistical classification of economic activities in the European community Rev. 2 classification, asmeasured by location quotients (LQs) for indicators on employment, firms’ incomes and number ofenterprises. However, there is a visible pattern that the LQs are higher in smaller geographic areas(NUTS 4 (Nomenclature of units for territorial statistics)), which usually represent big cities, beingthe cores of cluster initiatives. The study also discusses the phenomenon of the internationalizationof clusters and the value added to that process from forming formalized cluster initiatives, whichcreate favourable institutional framework for transborder cooperation.

Introduction

Clustering has become a very important topic, with clusters being seen as a key factor

influencing entrepreneurship (Pascal, 2005), innovativeness and regional development

(Porter, 1998, 2000). In advanced economies, economic activity tends to concentrate

around metropolitan areas and specialized regional clusters (Solvell et al., 2008,

p. 110). Clusters give competitive advantages to co-located firms due to the external econ-

omies of scale (Fujita et al., 2000), eased access to resources and proximity to specialized

suppliers and customers (Porter, 1998). Several authors (Porter, 2003; Ketels, 2009)

Correspondence Address: Arkadiusz Michał Kowalski, World Economy Research Institute, Warsaw School of

Economics, al. Niepodleglosci 162, 02-554 Warsaw, Poland. Email: [email protected]

European Planning Studies, 2014

Vol. 22, No. 1, 20–45, http://dx.doi.org/10.1080/09654313.2012.731040

# 2012 Taylor & Francis

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demonstrate a positive relationship between employment in strong clusters and economic

performance, meaning that regions with a higher level of specialization in an industry are

characterized by higher productivity in this industry. The elements that constitute clusters

are: a specific territory, a specialization in one or more segments of a supply chain and a

population of firms and institutions (Camuffo & Grandinetti, 2011, p. 815). On the other

hand, Anderson et al. (2004, p. 1) identify seven building blocks of clusters: “geographic

concentration; the core and defining specialization of clusters; the actors; dynamics and

linkages; critical mass; the cluster life cycle; and innovation”, while noting, however,

that not all these elements must be present in the case of each specific cluster.

The classical definition states that clusters are “geographic concentrations of intercon-

nected companies, suppliers, service providers, firms in related industries, and associated

institutions (e.g. universities, standards agencies, and trade associations) in particular

fields that compete but also cooperate” (Porter, 1998, p. 197). From the above definition,

we may derive two important characteristics of clusters:

. geographical concentration of companies and other actors in a specific sector, connected

with the phenomenon of the regional specialization,. co-opetition between cluster actors, encompassing both competition and cooperation.

In this article, we distinguish the term “cluster” from “cluster initiative”, which may be under-

stood as formal or non-formal activities undertaken by a group of enterprises and other units

cooperating with each other in some areas (Kładz & Kowalski, 2010). These two expressions

are interrelated, but they do not mean exactly the same. However, in practice, they are often used

interchangeably, which seems to be a mistake. In recent years, there has been a substantial

increase in the number of cluster initiatives in Poland. Although many of these projects

include the word “cluster” in their name, in reality, the market structures that are build are

far away from representing real clusters, fulfilling the above-mentioned criteria, especially geo-

graphical concentration of companies and other actors in a specific sector. Another term that is

often used in the literature is clustering (Akoorie, 2011; Cook & Pandit, 2012). However, it is

hard to find an exact definition of this expression. Taking into account, the previous differen-

tiation between clusters and cluster initiatives, it may seem ambiguous, which one of these

two does it relate to? Hence, in this study, we understand clustering as a process of forming

and developing cluster initiatives in the economy, implying some formal or non-formal activi-

ties undertaken by an organization or a group of organizations (like companies, scientific units,

public bodies, etc.) in order to stimulate cooperation between regional actors specializing in a

specific industries. One of the difficulties in these research areas is the ambiguity of the cluster

concept itself. According to some representatives of economic geography (Martin & Sunley,

2003, p. 9), “Porter’s cluster metaphor is highly generic in character, being sufficiently indeter-

minate to admit a very wide spectrum of industrial groupings and specialization”. They point out

the following questions, to which the cluster theory does not give a precise answer:

At what level of industrial aggregation should a cluster be defined, and what range of

related or associated industries and activities should be included? How strong do the

linkages between firms have to be? How economically specialized does a local con-

centration of firms have to be to constitute a cluster? (Martin & Sunley, 2003, p. 10)

The cluster concept gives little attention to the scale of geographical coverage of a group

without determining whether clusters exist nationally, regionally or locally (Perry, 2007).

Clusters versus Cluster Initiatives 21

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The difficulties in precisely addressing these challenges are reflected in Porter (1998) rec-

ognition that cluster boundaries

rarely conform to standard industrial classification systems, which fail to capture

many important actors in competition as well as linkages across industries ...

Because parts of a cluster often fall within different traditional industrial or service

categories, significant clusters may be obscured or even go unrecognized. (p. 204)

This impreciseness of the cluster concept, failing to define clear boundaries, both geo-

graphical and industrial, seems to be a gap in an existing economic theory. It puts three

main challenges for the present research:

. in which the level of geographic aggregation should the groupings of enterprises be ana-

lysed?. which sections of statistical classification of economic activities in the European com-

munity (NACE) should be taken into account when analysing clustering in particular

sectors of the economy?. to what extent cluster initiatives are overlapping with clusters, understood as real geo-

graphical concentrations of specific industries?

This article is structured as follows. The first section presents a literature review on the

impact of clusters on the competitiveness of the regional economy and of the affiliated

enterprises. Special focus is put on the importance of clustering for high-technology indus-

tries, like the Information and Communication Technology (ICT) sector, playing a key

role in efficient generation, using and in the dissemination of knowledge. This part pre-

sents also the concept of the cluster life cycle, showing that clustering is a dynamic

process and achieving a certain critical mass of the cluster initiative may take some

time. Next sections discuss the hypotheses, which are tested in this article and research

methods. The following part presents the general trends of clusters development in

Poland and the directions of economic policy actions in that field. Then, the study ident-

ifies 11 formal ICT cluster initiatives and verifies if they meet the Porter’s assumption of

geographical concentration and specialization. The final section presents some actions

connected with modern processes of cluster internationalization, with focus on some

experiences of the ICT West Pomerania Cluster.

Literature review on the process of clusters’ formation and the benefits from

clustering

When verifying if cluster initiatives may be treated as real economic structures, character-

ized by geographical concentration of companies and other actors in a specific sector, it is

worth to introduce a typology of clusters based on the concept of life cycle, which explains

cluster evolution in analogy to the product life cycle (Enright, 2003; Dalum et al., 2005).

According to this approach, cluster, like a product or even an industry, follows

cyclical development patterns. It means that clusters do not represent only temporary

solutions to actual problems, but they pass through a number of stages. Although

they may not be identical and the pace of their evolution depends on specific

circumstances, the life cycle of clusters can be said generally to undergo the stages below:

22 A.M. Kowalski & A. Marcinkowski

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. emerging cluster, containing a small number of the actors in the agglomeration, which start

to cooperate around a core activity, and realize common opportunities through their linkage,. growing cluster, attracting new actors in the same or related activities, with new linkages

developing between all these actors. In many cases, the cluster initiative develops its

label, website and common connotation,. mature cluster, which has reached a certain critical mass of actors and has developed

both internal and external relations outside of the cluster,. declining/transforming cluster, starting to experience a slowdown in growth and per-

formance, meaning that it has to undertake the transformation process and focus on

new growth factors, like the new market segment, new technology, new methods of

delivery of goods, new entrants to the cluster, etc.

The concept of the cluster life cycle means that clustering is a dynamic process and even if

the specific cluster initiative does not represent a remarkable regional specialization, it may

be a good starting point for developing the real industrial cluster. However, it is important to

note that before the emergence of a cluster, there should be a number of companies and other

actors specializing in one or few related industries, which may be called an agglomeration

stage. They are basic building blocks that make the existence of the cluster possible. A

cluster achieves inner dynamics if it engages numerous actors and reaches the so-called

critical mass. This is a concept that can be used with reference to various assets subject to

economies of scale and scope that can be made by a certain minimal concentration of

human and financial capital, knowledge, etc. (Anderson et al., 2004, p. 28).

Effectively, operating clusters may support businesses, especially small and medium

enterprises, to improve their competitive positions. Empirical results (Li & Geng, 2012)

show that the business performance of the cluster companies is significantly higher than

that of the non-cluster firms. In the literature, three broad dimensions are mentioned, in

which clusters influence competitiveness (Porter, 1998):

(1) increasing the efficiency and productivity of companies in the region, because of more

specialized assets and suppliers with shorter reaction times than they could in isolation,

(2) higher levels of innovation, because of close interactions with scientific units, other

enterprises and customers, knowledge spillovers, pressure to innovate and possibility

to share the costs of R&D,

(3) stimulating the formation of new businesses, which expand and strengthen the cluster

itself.

At the more detailed level, enterprises experience different kinds of microeconomic

benefits resulting from being a cluster member, among which the most important are

(Kowalski, 2011b):

. more opportunities to undertake joint R&D activities or other activities aiming at cre-

ation of innovation,. easier access to information on the market (e.g. the current needs of the customers) and

the latest technological advances,. more opportunities to identify market niches and access to export markets,. human capital development, as a result of greater mobility of staff and organized train-

ings and conferences,


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. greater access to scarce resources and skills, thanks to their complementarities in cluster

structures that facilitate mutual exchange or acquisition between partners (e.g. by cen-

tralized purchases),. increase in production capacity and operational flexibility through greater opportunities

to reallocate resources and to use vacant capacity of other economic entities operating in

the cluster,. opportunity to ensure complementarities of activities with other firms through better

matching of offers and the needs of businesses, more efficient roles and functions dis-

tribution between them or undertaking of joint marketing activities,. reducing the level of uncertainty and risk in business activity, by creating an atmosphere

of mutual trust in a changing market environment,. increasing the speed of action and enabling rapid response to signals from the business

environment.

Many economists highlighted the importance of clusters in high-technology industries

(Saxenian, 1994; Zucker et al., 1998; Bresnahan et al., 2001), which are based on the

transfer of scientific research results, as well as knowledge and technology into the

economy and which are characterized by high expenditures on R&D. These sectors, oper-

ating at the interface between science and industry, are treated as an important source of

economic development and contribute to the development of knowledge-based economy.

One of the most important challenges in the knowledge economy is efficient generation,

using and dissemination of knowledge. Since ICT sectors play a key role in these knowl-

edge processes, it is of crucial importance for economic development of regions and

countries. This is a reason for choosing ICT clusters in this paper as an area for analyses

of clustering processes in Poland. Moreover,

the information technology (IT) companies in clusters achieve tremendous pro-

ductivity gains and cost savings that come from sharing of resources, transfer of

knowledge and experiences, best practices, human resources, ready access to the

specialized services, availability of infrastructure, and even the cooperative strength

to build products for global markets. (Khomiakova, 2007, p. 356)

Valdaliso et al. (2011) found that the success of the electronics and ICT cluster of the

Basque Country has been determined by the benefits of an advanced absorptive capacity

created and sustained by a social capital, and by an internationalization process that indu-

cing an inflow of external knowledge into the cluster. Together with the development of

ICT and related opportunities for rapid transfer of innovative solutions to the competitors,

it becomes less possible for enterprises to protect their own knowledge, know-how and inno-

vation, by maintaining the confidentiality of the production. The competitive advantage is

determined rather by the ability to quickly innovate, which may be facilitated by opening up

to other entities participating in the cluster structure. The competitiveness of the regional

economy is determined by drivers of its innovation abilities, especially soft factors,

which also play an important role in the processes of clustering, like: a high quality of

human and social capital, activity of scientific and research units, entrepreneurship-friendly

environment, support from the local government and appropriate innovative milieu, as well

as creating platforms for knowledge transfer, sharing experiences and innovation diffusion.


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The impact of clusters on the innovativeness of the economy is influenced by the fact that

new technologies in specific sectors are created in units located in close proximity to

each other. Spatial proximity and cooperation among different cluster partners determine

the flow of knowledge and information, technology transfer, learning processes, as well

as generation and absorption of innovations. Regional aspects of innovation, like proximity

(cognitive, organizational, social, institutional, geographic) and the neighbourhood effect

polarizes the spatial structure of the economy and influences the concentration of socioeco-

nomic development, which results in forming and developing clusters (Kowalski, 2010c).

Research hypotheses

The motivation for cluster initiatives in Poland, as well as other EU countries, is very often

public policy programmes (which are presented in greater detail in Section Cluster devel-

opment and cluster policy in Poland), offering financial funds supporting different kinds of

actions connected with clustering processes. However, this may lead to a situation of

forming cluster initiatives in regions, which are not specializing in specific industries.

Porter (2003) states that the innovative processes in clusters depend on the cluster’s

size, the degree of its specialization, and the extent to which the region is focused upon

production in the relevant industries. In recent years, we have observed a dynamic increase

in the number of cluster initiatives in Poland, especially high-technology industries, like

ICT (they are presented in Section An analysis of LQs for ICT clusters in Poland). It

was supported by European programmes, especially aiming at internationalization of

cluster activities, which is a source of many benefits for participating companies, con-

nected, for example, with greater access to technology or new markets. An explanation

for that process and some examples of participation of Polish cluster initiatives in such

programmes, including the ICT sector, are presented in Section 7. The above-mentioned

problems lead us to formulate the following research hypotheses:

. Hypothesis 1: Economic policy instruments promoting cluster development in Poland

encourage enterprises to form cluster initiatives, especially in high-tech sectors.. Hypothesis 2: Not all emerging cluster initiatives meet the assumption of theoretical

cluster model on geographical concentration and specialization of firms in a specific

industry and related industries.. Hypothesis 3: The ICT sector, which is a pillar for knowledge-based economy, demon-

strates higher concentration and potential for clustering at the level of Polish big cities,

corresponding to NUTS 4 (Nomenclature of units for territorial statistics).. Hypothesis 4: An important value added for Polish entrepreneurs may come from

actions connected with internationalization of cluster initiatives, being a modern

phase of the evolution of clustering processes.

Method

Methodology for solving the main research problem, saying that not all emerging cluster

initiatives meet the assumption of the theoretical cluster model on the geographical

concentration and specialization of firms in a specific industry and related industries, is

based on statistical analyses of data using the location quotient (LQ). It is a measure devel-

oped in regional economics to identify economic structure and specialty (Isserman, 1977),


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with high values of this measure taken as evidence of a cluster or potential cluster (DTI,

2001; O’Donoghue & Gleave, 2004). LQ, being a ratio rather than an absolute number,

makes it possible to contrast the regional level against the national level, hence giving

an understanding of the relative sector size. Different studies (De Propris, 2005; Feser

& Isserman, 2009; Moral, 2009; Crawley & Hill, 2011) demonstrate the usefulness of

this technique in analysing economic agglomeration.

LQs are ratios that compare the concentration of a resource or activity in a defined area

to that of a larger, reference area, such as a country, region or subregion. The formula is as

follows:

LQi =xi/x( )

Xi/X( ) , (1)

where:

. LQi is the location quotient of industry i in the local region,

. xi is the value of an analysed indicator (for example, employment) of industry i in a

given region,. x is the total value of an analysed indicator in a given region,. Xi is a value of an analysed indicator in industry i in a reference area,. X is a total value of an analysed indicator in a reference area.

A value of LQ greater than 1 means that the local area has a relatively higher concen-

tration of economic activity in terms of the analysed indicator in a given industry than the

base area. However, in order to identify significantly high-geographical concentrations of

specific industries, we need to adopt a certain critical value, higher than 1. It is generally

accepted practice to interpret LQs greater than 1.25 as “high”. DTI (2001, p. 14) calls these

industries “high points” of the regional economy. This measurement of the significant

overrepresentation of an activity has clear implications for cluster analysis at both a theor-

etical and an empirical level, taking into account the main characteristics of the cluster

structure—geographical concentration and regional specialization. In the tables that are

presented in this article, the values surpassing value 1.25 are marked by “∗”. However,

in some cases, the LQs are much greater than this critical value. Therefore, we set up

the second threshold for the critical value equal to 2 marked by “∗∗”, showing very

high concentration of a specific type of economic activity.

As an answer to the challenges connected with measuring geographic concentrations of

industries, which were described in the introduction of the article, we take into consider-

ation statistical data on different levels of geographic units in Poland (NUTS levels: 2, 3

and 4). It is connected with the fact that identified cluster initiatives have different geo-

graphic extent, including companies from one powiat (NUTS 4), grouping of powiats

(subregions—NUTS 3), the whole voivodship (NUTS 2) or even few voivodships in

some cases. With regard to industry boundaries, since this study considers ICT clusters,

it takes into account LQs (all data are for 2008) for (according to NACE Rev. 2):

. division 26 called “Manufacture of computer, electronic and optical products”, includ-

ing, for example, manufacture of office machinery and computers,. following divisions included in Section J “Information and communication”:. division 58 “Publishing”, including software publishing,. division 61 “Telecommunications”,


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. division 62 “Computer programming, consultancy and related activities”,

. division 63 “Information service activities”.

Cluster development and cluster policy in Poland

In recent years, there is an increase of interest in clusters in Poland, especially in terms of

the innovation policy. According to OECD (2005, p. 12), emerging regional innovation

systems in Polish regions show a strong similarity to clusters, mainly in high-technology

sectors. However, clusters in Poland are formed in modern, as well as in traditional sectors,

such as: agriculture, food, coal mining, textile or wood and furniture industries. Clustering

processes play an important role in all these areas since they encourage entrepreneurship,

contributing to higher productivity and the competitive advantage of the firms. The fact

that a specific cluster operates in traditional industries does not mean that it does not influ-

ence generation, using and dissemination of knowledge. On the contrary, cooperation with

scientific units and other firms in the framework of cluster structures opens up new devel-

opment possibilities for units operating also in low-technology sectors.

Despite the strong intensification of the competition in both national and global markets,

cooperation may be treated as one of the key factors of business success and a crucial

characteristic of entrepreneurship. Cooperation between different actors does not

exclude their competition at the same time, which is understood as “co-opetition”. One

of the formal definitions of co-opetition states that it is the phenomenon by which

“firms in the same industry complete each other in creating markets but compete in divid-

ing up markets” (Chesbrough et al., 2006, p. 87). Co-opetition, which emerges when

different firms simultaneously compete and cooperate with each other, is quite common

in mature and high-tech industries (Schiavone & Simoni, 2011). Cooperation, through a

more intensive exchange of knowledge, experiences and best practices, technology trans-

fer, exploitation of scarce resources and increasing the degree of specialization, allows

enterprises to overcome their isolation and reach a collective competitive advantage,

which is beyond the reach of the individual firm. However, one of the biggest weaknesses

of the National Innovation System in Poland is poor cooperation between companies and

between entrepreneurs and scientists (Ministry of Economy, 2006). One of the reasons is

an extremely low level of social trust in Poland. The percentage of Polish residents aged 16

or over that trust other people is 13, whereas the average percentage for 27 European

countries equals 32 (Czapinski & Panek, 2011). One of the possible solutions that may

increase cooperation is developing clusters, which are an effective tool for pooling

resources and human capital accumulation, facilitating the achievement of an appropriate

critical mass. The question is if, and if so, to what degree, should the government intervene

in the area of clustering. The characteristic of the clustering model in Poland is the

bottom–up approach, meaning that cluster initiatives are not created from scratch of gov-

ernment intervention, but they should result from entrepreneurs’ initiative and activities,

occasionally supported by public programmes when necessary. It is widely recognized

that an element, which plays a crucial role in cluster development and its increasing

success, especially in the initial stages of the life cycle, is entrepreneurial activity

(Butel & Watkins, 2006, p. 257). Policy intervention may be relatively effective in sup-

porting some elements of clusters, like the development of a cluster-focused physical

infrastructure, but it is useless in many other aspects of clustering, like the replication

of network-based resources and institutional endowment (Duranton et al., 2010).


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Such an understanding of clusters answers very well to the paradigm of endogenous

growth and is a reason for developing the cluster policy in Poland, which signs into the

concept of cluster-based policy, originated from the OECD (Roelandt & den Hertog,

1999). This approach has its origins in the theory on growth poles (Perroux, 1950),

which assumes that “growth does not appear everywhere at the same time; it manifests

itself in points or poles of growth, with variable intensities; it spreads by different channels

with variable terminal effects for the economy as a whole” (Perroux, 1955, p. 56). Growth

poles are understood as concentrations of industries around a central core that are able to

impact growth for them, as well as that of the surrounding area. Supporting clusters is also

becoming an important element in the EU economic policy, being among the priorities of

the Europe 2020 Strategy that takes in intelligent and sustainable development, while also

striving for social inclusion. One of the objectives of this strategy is to “improve the

business environment, especially for SMEs, including through reducing the transaction

costs of doing business in Europe, the promotion of clusters and improving affordable

access to finance” (European Commission, 2010, p. 17).

The beginning of the cluster policy in Poland is dated to early 2000s, when there was

an analysis of industrial policy clusters and potential policy instruments conducted. The

importance of supporting clusters was highlighted in “The Strategy for Increasing

the Innovativeness of the Economy for 2007–2013”, strategic document accepted by the

Council of Ministers in September 2006 (Ministry of Economy 2006). In the Direction 5

of the strategy, called “Infrastructure for innovation”, there is an objective to improve the

conditions for innovative enterprises, focusing on, among others, supporting network

cooperation and the development of clusters and technological platforms in technologically

advanced sectors, as well as strengthening the cooperation between R&D sector and the

economy. The implementation of the strategy is based on an implementing system of oper-

ational programmes within “The National Strategic Reference Framework 2007–2013”,

with particular importance of “Operational Programme Innovative Economy, 2007–

2013” (Ministry of Regional Development, 2007). In this programme, there is an Action

5.1 directly connected with clusters, including support for investments and counselling ser-

vices related to development of cooperative relations on a supra-regional scale. The new

“Strategy for Innovative and Efficient Economy” puts the main objective of creating

highly competitive economy based on the knowledge and cooperation (Ministry of

Economy, 2012). It enhances the role of clusters by adopting as one of its principles partner-

ship cooperation, involving developing entrepreneurs’ relationships with the business and

social environment. Cluster initiatives are also supported by regional operational pro-

grammes at the level of Polish voivodship (NUTS 2) (Kowalski, 2010a). Thanks to that,

cluster structures may act as growth poles for the economy, and be the sources of regional

development impulses spreading to the surrounding areas. The resulting competitive advan-

tage of the location can be revealed at the national and often international level.

An analysis of LQs for ICT clusters in Poland

There are 11 formal ICT cluster initiatives in Poland that have been identified by the

authors of this article, on the basis of different reports (Deloitte, 2010; Kładz & Kowalski,

2010) and Internet research. The objective of this study is to verify to what extent they may

be regarded as real clusters, characterized by geographical concentration of ICT-related

activities in the areas where they are developed:


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1. Wielkopolska ICT Cluster (http://wklaster.pl/en/),

2. ICT West Pomerania Cluster (http://klaster.it/),

3. ICT Pomerania Cluster (http://www.pomorski-klaster-ict.pl),

4. Mazowieckie ICT Cluster (http://klasterict.pl/),

5. Alternatywny.klaster.info, Warsaw (http://klaster.info/),

6. The Cluster of Multimedia and Information Systems “MultiCluster”, Nowy Sacz

(http://multiklaster.pl),

7. Malopolskie Cluster of Information Technologies (http://www.klaster.krakow.pl/),

8. Malopolskie Informatics Cluster “EKlaster” (http://www.eklaster.org/),

9. Informatics cluster New Technology Hills, “NT Hills”, Bielsko-Biala (http://nthills.pl),

10. Eastern Poland Informatics Cluster (http://www.klasterit.pl/),

11. Cluster “Knowledge and Innovation Community for Information and Communication

Technologies”, Wroclaw (http://www.ict-cluster.wroc.pl).

In Table 1, there are LQs presented for employment. When selecting specific regions to be

indicated in this table, we concentrated especially on these NUTS 3 and NUTS 4 regions, for

which the data confirms a high concentration of ICT-related activities, so there may be

different numbers of regions assigned to different cluster initiatives.

When analysing LQs for employment, it is possible to confirm the concentration of ICT-

related activities in the regions, in which formal cluster initiatives are set up in following

cases:

. Wielkopolska ICT Cluster, but mostly at the local level (NUTS 4—Poznan city), with

very high levels of LQs (above 2) for NACE Rev. 2 divisions 58 and 62 and with

high LQ (above 1.25) for division 63,. ICT West Pomerania Cluster, especially at the NUTS 3 level (Szczecinski subregion)

with very high level of LQ (above 2) for division 26, as well as at the local level

(NUTS 4), namely in cities: Szczecin (with a very high level of LQ (above 2) for div-

isions 62 and 63, and Police (with a very high LQ (above 2) for division 26),. ICT Pomerania Cluster, especially at the NUTS 3 level (Tricity subregion) with a very

high level of LQ (above 2) for division 63 and with high LQs (above 1.25) for divisions:

26, 58 and 62, as well as at the local level (NUTS 4), Gdansk city, with high LQs (above

1.25) for divisions: 26, 58, 62 and 63, as well as for some other NUTS 4 regions, like

powiat kwidzynski or powiat tczewski,. ICT clusters in Mazovia voivodship (NUTS 2) in general, with very high LQs (above 2)

for divisions: 58, 61 and 63, and with high LQs (above 1.25) for division 62, and

Warsaw (NUTS 4) in particular, with very high LQs (above 2) for divisions: 58 , 61,

62 and 63,. ICT cluster in Malopolskie voivodship (NUTS 2), but mostly at the level of Cracow, the

second biggest city in Poland (NUTS 4), with very high LQs (above 2) for divisions 62,

and 63, as well as with high LQ (above 1.25) for division 58,. Cluster “Knowledge and Innovation Community for ICT” in Lower Silesia voivodship,

at the NUTS 3 level (Wroclaw subregion) with a very high level of LQ (above 2) for

division 26, and at the local level (NUTS 4) with high LQs (above 1.25) for divisions:

58, 61 and 62 for Wroclaw city, as well as with a very high level of LQ (above 2) for

division 26 for powiat trzebnicki and swidnicki.


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http://wklaster.pl/en/

http://klaster.it/

http://www.pomorski-klaster-ict.pl

http://klasterict.pl/

http://klaster.info/

http://multiklaster.pl

http://www.klaster.krakow.pl/

http://www.eklaster.org/

http://nthills.pl

http://www.klasterit.pl/

http://www.ict-cluster.wroc.pl

Table 1. LQs for employment for regions with ICT cluster initiatives in Poland

Cluster initiative Regional level

LQs for divisions according toNACE Rev. 2

26 58 61 62 63

1 Wielkopolska ICTCluster

NUTS 2—Wielkopolskie voiv. 0.39 0.94 0.22 0.80 0.53NUTS 3—Poznanski subregion 0.47 1.19 0.11 0.55 0.37NUTS 4—Poznan city powiat 0.93 2.45∗∗ 0.42 2.40∗∗ 1.52∗

NUTS 4—Powiat sremski 4.46∗∗ 0.22 0.01 0.32 0.152 ICT West Pomerania

ClusterNUTS 2—West Pomernia voiv. 0.92 0.47 0.37 1.51∗ 0.97NUTS 3—Szczecinski subregion 2.31∗∗ 0.21 1.15 0.29 0.22NUTS 4—Powiat policki 7.47∗∗ 0.29 0.12 0.38 0.15NUTS 4—Szczecin city powiat 0.81 1.13 0.86 2.18∗∗ 2.58∗∗

3 ICT Pomerania ClusterTricity subregion

NUTS 2—Pomerania voiv. 3.01∗∗ 0.87 0.37 0.88 1.00NUTS 3—Tricity subregion 1.35∗ 1.47∗ 0.67 1.64∗ 2.27∗∗

NUTS 4—Gdansk city powiat 1.43∗ 1.45∗ 0.77 1.68∗ 1.94∗

NUTS 3—Starogardzkisubregion

11.09∗∗ 0.75 0.10 0.21 0.12

NUTS 4—Powiat kwidzynski 23.69∗∗ 0.05 0.05 0.12 0.18NUTS 4—Powiat tczewski 18.35∗∗ 2.42∗∗ 0.17 0.23 0.13

4 Mazovia ICT Cluster NUTS 2—Mazovia voiv. 1.17 2.74∗∗ 3.60∗∗ 1.68∗ 2.41∗∗

NUTS 3—West Warsawsubregion

3.48∗∗ 0.92 0.49 0.93 0.42

Alternatywny.klaster.info,Warsaw

NUTS 4—Powiat grodziski 5.37∗∗ 0.56 0.14 0.85 0.42NUTS 4—Powiat piaseczynski 5.94∗∗ 1.31 0.43 1.22 0.63NUTS 4—Powiat zyrardowski 8.84∗∗ 0.29 0.51 0.47 0.24NUTS 4—Warsaw city powiat 0.89 4.08∗∗ 5.75∗∗ 2.42∗∗ 3.82∗∗

5 The Cluster of Multimediaand InformationSystems“MultiCluster”, NowySacz

NUTS 2—Malopolskie voiv. 0.64 0.97 0.71 1.22 1.57∗

NUTS 3—Nowosadeckisubregion

0.11 0.27 0.24 0.35 0.18

NUTS 4—Powiat nowosadecki 0.02 0.19 0.21 0.26 0.37NUTS 4—Nowy Sacz city

powiat0.17 0.65 0.58 0.55 0.14

6 Malopolskie “EKlaster” NUTS 2—Malopolskie voiv. 0.64 0.97 0.71 1.22 1.57∗

NUTS 3—Cracow subregion 0.51 0.62 0.57 0.45 0.65Malopolskie Cluster of IT NUTS 4—Powiat krakowski 0.72 0.75 0.49 0.65 1.27∗

NUTS 4—Cracow city powiat 0.69 1.89∗ 1.20 2.52∗∗ 3.54∗∗

7 Informatics cluster “NTHills”, Bielsko-Biala

NUTS 2—Slaskie voiv. 0.41 0.58 0.60 0.90 0.78NUTS 3—Bielski subregion 0.19 0.51 0.21 0.56 0.37NUTS 4—Powiat bielski 0.25 0.37 0.11 0.43 0.16NUTS 4—Bielsko Biala city p. 0.11 0.68 0.25 0.82 0.60

8 Eastern PolandInformatics Cluster(with central office inRzeszow)

NUTS 2—Podkarpackie voiv. 0.46 0.47 0.32 1.20 0.32NUTS 3—Rzeszowski subregion 0.24 0.76 0.45 3.11∗∗ 0.29NUTS 4—Powiat kolbuszowski – 2.07∗∗ 0.26 0.39 0.08NUTS 4—Powiat rzeszowski 0.30 0.02 1.27∗ 0.63 0.12NUTS 4—Reszow city powiat 0.34 1.18 0.30 5.44∗∗ 0.37

9 Cluster “Knowledge andInnovation Communityfor ICT”, Wroclaw

NUTS 2—Lower Silesia voiv. 1.79∗ 0.56 0.68 0.89 0.48NUTS 3—Wroclaw subregion 6.47∗∗ 0.20 0.36 0.44 0.19NUTS 4—Powiat wroclawski 20.85∗∗ 0.33 0.87 0.70 0.18NUTS 4—Wroclaw city powiat 0.65 1.35∗ 1.46∗ 1.96∗ 0.67NUTS 4—Powiat trzebnicki 3.45∗∗ 0.17 0.09 0.42 0.28NUTS 4—Powiat swidnicki 3.29∗∗ 0.14 0.06 0.34 1.02

Source: Own compilation based on Central Statistical Office data.


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An interesting overview of Polish cluster initiatives (marked by circles from 1 to 9, cor-

responding to cluster initiatives listed in Table 1) and concentration of ICT-related

NACE Rev. 2 divisions in NUTS 4 regions for employment is provided on the map pre-

sented in Figure 1. This map shows the highest value of LQs for the following divisions:

26, 58, 61, 62 and 63 in all Polish NUTS 4 regions. If LQ for all divisions for specific

NUTS 4 region is less than 1, then this region is marked in white. LQs shown in Figure

1 may be explained by the following equation:

LQ empi = max(LQ empij), (2)

where

. LQ empi is the location quotient for employment for specific NUTS 4 region marked in

the colour according to the legend,. i indicates specific NUTS 4 region,

. j indicates NACE Rev. 2 divisions, j ¼ {26, 58, 61, 62, 63}.

Figure 1. LQs for employment for Polish NUTS 4 regions in the background of formal clusterinitiatives.


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Most of the studies measuring LQs take into account indicators connected with employ-

ment. However, it is possible to calculate the LQs for other indicators, out of which the

number of firms and companies’ incomes seems to be of particular relevance for measur-

ing the clustering potential in different regions. LQs for indicator on firm’s incomes for

areas with ICT cluster initiatives in Poland are presented in Table 2.

When analysing data for firms’ incomes, we may observe especially high values for LQs

in Mazovia voivodship, especially its centre—Warsaw, with very high LQs (above 2) for

NACE divisions included in Section J “Information and communication”: 58, 61, 62 and

63. It is connected to the fact that many companies report their earnings in their main seats,

which are usually set up in the capital city of the country, even if the production or other

kinds of economic activity take place elsewhere. Main seats of different companies may

also be set up in other big cities, especially Cracow, the second biggest city in Poland,

which is confirmed by very high LQs (above 2) for divisions: 62 and 63, and by a high

LQ (above 1.25) for division 58.

An illustration of Polish cluster initiatives and concentration of ICT-related NACE Rev.

2 divisions in NUTS 4 regions for firms’ incomes is presented in Figure 2, which is con-

structed according to the same methodology as Figure 1. LQs marked in Figure 2 are

explained by the following equation:

LQ inci = max(LQ incij), (3)

where

. LQ inci is the location quotient for firms’ incomes for the specific NUTS 4 region

marked in colour according to the legend,. i indicates specific NUTS 4 region,

. j indicates NACE Rev. 2 divisions, j ¼ {26, 58, 61, 62, 63}.

The results of the analysis for LQs for the third indicator, the number of companies, are

shown in Table 3.

When analysing LQs for the number of firms, we may observe general consistency with

the findings for employment from Table 1. The results indicate the concentration of ICT-

related activities in the regions, in which formal cluster initiatives are set up in the follow-

ing cases:

. Wielkopolska ICT Cluster, but at the local level (NUTS 4—Poznan city), with high LQs

(above 1.25) for divisions: 58, 61 and 63,. ICT West Pomerania Cluster, but only at the local NUTS 4 level (Szczecin city), with a

high level of LQ (above 2) for division 62,. ICT Pomerania Cluster, especially at the NUTS 3 level (Tricity subregion) and NUTS 4

(Gdansk and Gdynia cities), with high LQs (above 1.25) for divisions: 26, 58, 61 and

only in case of Gdynia for division 63,. ICT clusters in Mazovia voivodship (NUTS 2) in general, with high LQs (above 1.25)

for divisions: 26, 58, 61 and 63, as well as at local NUTS 4 levels in particular, especially

for Warsaw city, with very high LQs (above 2) for divisions: 58, 61 and 63, and with

high LQs (above 1.25) for divisions: 26 and 62,


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Table 2. LQs for firms’ incomes for regions with ICT cluster initiatives in Poland

Cluster Regional level

LQs for divisions according to NACERev. 2

26 58 61 62 63

1 Wielkopolska ICT Cluster NUTS 2—Wielkopolskievoiv.

0.15 0.85 0.06 0.87 0.33

NUTS 3—Poznanskisubregion

0.12 1.01 0.02 0.43 0.12

NUTS 4—Powiatpoznanski

0.07 1.39∗ 0.01 0.53 0.09

NUTS 4—Poznan citypowiat

0.28 1.70∗ 0.15 2.22∗∗ 0.71

2 ICT West PomeraniaCluster

NUTS 2—West Pomerniavoiv.

0.78 0.33 0.09 0.66 1.58∗∗

NUTS 3—Szczecinskisubregion

2.15∗∗ 0.11 0.02 0.14 0.02

NUTS 4—Powiat policki 4.93∗∗ 0.13 0.01 0.20 0.03NUTS 4—Szczecin city

powiat0.43 0.83 0.25 1.69 4.77∗∗

3 ICT Pomerania Cluster NUTS 2—Pomeraniavoiv.

2.86∗∗ 1.24 0.18 0.81 0.98

NUTS 3—Tricitysubregion

0.43 1.00 0.28 1.20 1.60∗

NUTS 4—Powiat gdanski 3.21∗∗ 1.47∗ 0.07 0.48 0.13NUTS 3—Podregion

Starogard14.68∗∗ 3.51∗∗ 0.02 0.13 0.04

NUTS 4—Powiatkwidzynski

21.50∗∗ 0.05 0.01 0.07 0.04

NUTS 4—Powiattczewski

22.40∗∗ 10.17∗∗ 0.04 0.09 0.04

4 Mazowieckie ICT Cluster NUTS 2—Mazovia voiv. 0.63 2.07∗∗ 3.15∗∗ 1.55∗ 1.76∗


NUTS 3—West Warsawsubregion

2.72∗∗ 1.13 0.17 0.82 0.26

NUTS 4—Powiatgrodziski

6.51∗∗ 0.22 0.01 0.67 0.23

NUTS 4—Powiatpiaseczynski

3.50∗∗ 1.24 0.17 1.76∗ 0.43

NUTS 4—Powiatpruszkowski

3.12∗∗ 0.41 0.14 0.51 0.25

NUTS 4—Powiatzyrardowski

0.93 6.62∗∗ 0.11 0.26 0.03

NUTS 4—Warsaw citypowiat

0.62 3.30∗∗ 5.36∗∗ 2.47∗∗ 2.97∗∗


NUTS 2—Malopolskievoiv.

0.51 0.94 0.20 1.38∗ 2.33∗∗


0.14 0.42 0.05 0.26 0.31

NUTS 4—Powiatnowosadecki

0.08 0.13 0.03 0.25 0.34

NUTS 4—Nowy Sacz citypowiat

0.21 1.40∗ 0.11 0.42 0.05

(Continued)


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. ICT cluster in Malopolskie voivodship (NUTS 2), but only at the level of Cracow, the

second biggest city in Poland (NUTS 4), with very high LQs (above 2) for divisions: 26

and 61, and high LQs (above 1.25) for divisions: 58, 62 and 63,. Eastern Poland Informatics Cluster with the central office in Rzeszow, mostly at the

local level (NUTS 4—Rzeszow city), with a very high LQ (above 2) for division 63,

and high LQs (above 1.25) for divisions: 26, 58 and 62. It is noteworthy that the

cluster initiative is developed in the framework of the project co-financed from the

Eastern Poland Operational Programme, which was awarded from the European

Regional Development Fund for the five most disadvantaged Polish regions: Lubelskie,

Podkarpackie, Podlaskie, Swietokrzyskie and Warminsko-Mazurskie. These regions are

characterized by a low dynamic of their economic development, poorly developed and

inadequate transport infrastructure and insufficient growth factors. However, the devel-

opment of a strong grouping of ICT companies centred in Rzeszow city may become a

growth pole for the whole region, as suggested, and presented earlier in this article, by

the Perroux theory of growth poles (Perroux, 1950),

Table 2. Continued



26 58 61 62 63

6 Malopolskie “EKlaster” NUTS 2—Malopolskievoiv.

0.51 0.94 0.20 1.38∗ 2.33∗∗

Malopolskie Cluster of IT NUTS 3—Cracowsubregion

0.26 0.37 0.11 0.44 0.28

NUTS 4—Cracow citypowiat

0.53 1.87∗ 0.40 2.98∗∗ 5.59∗∗


NUTS 2—Silesia voiv. 0.36 0.33 0.11 0.68 0.41NUTS 3—Bielski

subregion0.09 0.24 0.03 0.29 0.11

NUTS 4—Powiat bielski 0.11 0.07 0.02 0.17 0.02NUTS 4—Bielsko Biala

city powiat0.05 0.32 0.02 0.27 0.14


NUTS 2—Podkarpackievoiv.

0.39 0.28 0.06 1.96∗ 0.17

NUTS 3—Rzeszowskisubregion

0.18 0.36 0.10 5.82∗∗ 0.17

NUTS 4—Reszow citypowiat

0.26 0.53 0.06 10.30∗∗ 0.22


NUTS 2—Lower Silesiavoiv.

2.83∗∗ 0.55 0.33 0.90 0.38

NUTS 3—Wroclawsubregion

8.32∗∗ 0.11 0.04 0.22 0.06

NUTS 4—Powiattrzebnicki

3.50∗∗ 0.13 0.02 0.35 0.12

NUTS 4—Powiatwrocławski

16.96∗∗ 0.12 0.06 0.20 0.04



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† Cluster “Knowledge and Innovation Community for ICT” in Lower Silesia, mostly at

the local level (NUTS 4—Wroclaw city), with very high LQs (above 2) for divisions: 26

and 61, and high LQs (above 1.25) for divisions: 58 and 63.

The map presented in Figure 3, showing LQs for the indicators on a number of firms in

the background of cluster initiatives in Poland, applies the same methodology as maps in

Figures 1 and 2. LQs marked in Figure 3 are explained by the following equation:

LQ numi = max(LQ numij), (4)

where

. LQ numi is location quotient for a number of firms for the specific NUTS 4 region

marked in colour according to the legend,. i indicates the specific NUTS 4 region,

. j indicates NACE Rev. 2 divisions, j¼ {26, 58, 61, 62, 63}.

Figure 2. LQs for firms’ incomes for Polish NUTS 4 regions in the background of formal clusterinitiatives.


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Table 3. LQs for number of firms for regions with ICT cluster initiatives in Poland



26 58 61 62 63

1 Wielkopolska ICTCluster

NUTS 2—Wielkopolskievoiv.

0.71 0.81 0.77 0.94 0.95

NUTS 3—Poznanskisubregion

0.67 0.82 0.59 0.89 0.94

NUTS 4—Poznancity powiat

1.13 1.55∗ 0.75 1.72∗ 1.61∗

2 ICT West PomeraniaCluster

NUTS 2—WestPomernia voiv.

0.71 0.64 0.77 0.85 0.78

NUTS 3—Szczecinskisubregion

0.58 0.57 0.62 0.63 0.52

NUTS 4—Szczecincity powiat

0.99 0.99 1.01 1.33∗ 1.23

3 ICT PomeraniaCluster

NUTS 2—Pomeraniavoiv.

1.07 0.87 0.90 0.95 0.78

NUTS 3—Tricitysubregion

1.49∗ 1.36∗ 1.13 1.43∗ 1.18

NUTS 4—PowiatGdansk city

1.42∗ 1.39∗ 1.24 1.50∗ 1.14

NUTS 4—PowiatGdynia city

1.53∗ 1.31∗ 0.95 1.39∗ 1.40∗

NUTS 4—PowiatSopot city

1.92∗ 1.33∗ 0.96 1.11 0.66

NUTS 3—PodregionGdanski

0.73 0.56 0.52 0.54 0.43

NUTS 4—Powiatgdanski

1.36∗ 0.65 1.09 0.89 0.78

NUTS 4—PowiatGdansk city

1.07 0.87 0.90 0.95 0.78

4 Mazowieckie ICT Cluster NUTS 2—Mazoviavoiv.

1.34∗ 1.88∗ 1.20 1.56∗ 1.60∗

NUTS 3—WestWarsaw subregion

1.68∗ 1.44∗ 0.91 1.39∗ 1.36∗


NUTS 4—Powiatgrodziski

2.08∗∗ 1.15 0.70 1.48∗ 1.47∗

NUTS 4—Powiatpiaseczynski

1.86∗ 2.06∗∗ 0.85 1.61∗ 1.67∗

NUTS 4—Powiatsochaczewski

1.09 0.65 2.12∗∗ 0.82 0.85

NUTS 4—Powiatwarszawskizachodni

2.45∗∗ 1.61∗ 0.95 1.48∗ 1.53∗

NUTS 4—Warsawcity powiat

1.56∗ 2.77∗∗ 1.42∗ 2.09∗∗ 2.23∗∗

(Continued)


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Figure 4 combines all previous maps and it takes into account LQs for the indicators on

employment, firm’s incomes and a number of firms in the background of cluster initiatives

in Poland. LQs are marked in this map for regions, in which LQs for all indicators are

higher than the critical value. In other words, specific NUTS 4 region in Figure 4 is

Table 3. Continued



26 58 61 62 63


NUTS 2—Malopolskie voiv.

1.10 1.00 1.04 0.82 0.85


0.35 0.33 0.53 0.42 0.42

NUTS 4—Powiatnowosadecki

0.09 0.25 0.65 0.34 0.56

NUTS 4—Nowy Saczcity powiat

1.48∗ 0.70 1.01 0.93 0.74

6 Malopolskie “EKlaster” NUTS 2—Malopolskie voiv.

1.10 1.00 1.04 0.82 0.85

Malopolskie Cluster of IT NUTS 3—Cracowsubregion

0.84 0.65 0.76 0.57 0.64

NUTS 4—Cracowcity powiat

2.01∗∗ 2.12∗∗ 1.32∗ 1.48∗ 1.5∗


NUTS 2—Silesiavoiv.

1.21 0.85 1.24 0.97 0.97

NUTS 3—Bielskisubregion

0.80 0.80 0.86 0.87 0.85

NUTS 4—Powiatbielski

1.40∗ 0.53 0.64 0.72 0.58

NUTS 4—BielskoBiala city powiat

0.52 1.20 0.97 1.34∗ 1.15


NUTS 2—Podkarpackie voiv.

0.62 0.60 1.23 1.04 0.85

NUTS 3—Rzeszowskisubregion

0.93 0.70 1.30 1.55 0.88

NUTS 4—Powiatlancucki

0.66 0.47 1.37∗ 1.05 1.45∗

NUTS 4—Reszowcity powiat

1.43∗ 1.14 1.27∗ 2.17∗∗ 1.22


NUTS 2—LowerSilesia voiv.

1.19 1.06 0.95 1.10 1.01

NUTS 3—Wroclawsubregion

1.02 0.69 0.73 0.89 0.81

NUTS 4—Powiatwroclawski

2.37∗∗ 1.29∗ 0.97 1.39∗ 1.04

NUTS 4—Wroclawcity powiat

2.02∗∗ 2.09∗∗ 1.19 1.81∗ 1.31∗



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marked if it is marked in all Figures 1–3. LQs marked in Figure 4 are explained by the

following equation:

LQ mini= min (LQ empi, LQ inci, LQ numi). (5)

The above analysis indicates that not all ICT cluster initiatives in Poland represent real

concentrations of such activities, as: manufacture of computer, electronic and optical pro-

ducts, publishing, including software publishing, telecommunications, computer program-

ming, consultancy and related activities or information service activities. There is a visible

pattern that together with analysing the smaller geographic area, the LQs are becoming

higher. It is connected to the fact that these smaller areas usually represent big cities

that act as a core of cluster initiatives with most of the firms operating in them, even if

there are some member companies located in more distant places of a voivodship. We

can also observe that there is a potential to form ICT cluster initiatives in some regions

that have been lacking such formal initiatives until now, for example, in case of Lodz

city or torunski powiat.

Figure 3. LQs for number of firms for Polish NUTS 4 regions in the background of formal clusterinitiatives.


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Internationalization of Polish clusters

An important trend in clustering in recent years has been the process of the internationa-

lization of clusters. In the traditional approach, clusters were regarded as closed production

systems limited to a specific location, in which interactions could occur only at the begin-

ning and end of the chain. With the globalization of the world economy and increased

specialization in the value chain across national borders, cluster initiatives also opened

to foreign partners and international collaboration. This process indicates that clusters

entered into the next phase of evolution. After local clustering, taking place between

actors located in one region, it is time to create cooperative relations on a supra-regional

and transnational networks, and establish cross-border clusters (Kowalski, 2010b).

Recent research on how globalization impacts the Italian districts suggests that

increased participation in global value chains, linking to customers and suppliers

outside the cluster, is becoming increasingly important for achieving and maintaining

competitiveness (Arikan & Schilling, 2011). Clusters are taking on new international

strategies, such as outsourcing and foreign direct investment to maintain their

Figure 4. Polish NUTS 4 regions with LQs for employment, firm’s incomes and a number of firmssurpassing critical value in the background of formal cluster initiatives.


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competitive ability (Rabellotti et al., 2009). With the increasing ability of ICT to underpin

co-ordination, the role of proximity between different companies and other units is chal-

lenged. In an increasing number of industries, with easy access to manufacturing resources

in low-cost countries and decreasing transportation costs, manufacturing is relocating

(Pilat et al., 2008, p. 103). This observation gives a reason to reconsider the role of clusters

in shaping competitiveness, suggesting that conventional models of the major forces

driving the clustering of economic activities should be rethought (Dunning, 2002). The

process of globalization has influenced clusters and other local production systems to

open up their borders and to increase their linkages with actors outside their regions. In

modern global economy, the notion of a cluster as a self-contained knowledge hub, incor-

porating strong internal knowledge exchange and little interaction with the outside world,

is under pressure. Scholars increasingly recognize the division of knowledge work and

specialization across clusters, where openness to external knowledge is increasingly

important following globalization (Isaksen & Kalsaas, 2009). Firms and clusters have

gone international, searching for new sources of knowledge, new markets and lower

labour costs. It is possible to analyse two main areas in which clusters may play an impor-

tant role in the process of a firm’s internationalization (Kowalski, 2011a):

(1) being a member of the cluster initiative influences company’s internationalization be-

haviour, encouraging it to establish business relations in foreign markets,

(2) clusters may increase location attractiveness of the regions, attracting foreign direct

investments.

Stimulating a more international orientation of clusters, by encouraging stronger links with

foreign cluster initiatives, firms, research organizations and technology providers and by

attracting more foreign direct investment, especially in knowledge-intensive sectors, is one

of the most important directions of cluster development policy in Poland. There are different

actions undertaken with the objective of promoting trans-national cooperation of clusters. As

an example, Poland takes part in PRO INNO European Cluster Alliance, under PRO-INNO

Europe Initiative. In particular, Polish Ministry of Economy participated in BSR InnoNet pro-

gramme, which fostered better trans-national coordination and integration of innovation pro-

grammes, consequently reducing fragmentation in the Baltic Sea Region. Polish clusters were

included into the sub-component of this programme—Pilot Programmes on Innovation

systems and Clusters (PIC),1 constituting a base for learning and knowledge development

regarding the design of the programmes on innovation and clusters, as indicated in Table 4.

As can be seen from the above table, Polish clusters were taking part in all four PICs.

One of the most active cooperation was undertaken in the programme focusing on the ICT

sector (entire value chain of mobile devices), in which ICT West Pomeranian Cluster was

taking part. The general objective of the pilot was to develop a sustainable collaboration of

the ICT clusters in the Baltic Sea Region in order to support the growth potential within the

area. The specific actions aimed to:

. contribute to a sustainable environment by using ICT solutions for low energy living,

. develop models on how to support regional ICT companies at European trade fairs,

. encourage female leadership within the ICT industry in the BSR, and

. build a sustainable platform for internal and external knowledge-sharing and communi-

cation.


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One of the biggest achievement of the programme was a common participation of its

participants in the international trade fair, called Mobile World Congress in Barcelona

in February 2009, together with organization of a roundtable meeting. According to the

literature (Ramırez-Pasillas, 2010), taking part in international fairs is a kind of activity

that helps clustered firms to create global networked structures that are not geographically

anchored. It is not even necessary for all firms to engage in this kind of event. However,

sharing with other network members the information and ideas generated at the fair is a

central activity for the dissemination of knowledge on clusters.

Running all four PICs has resulted in many experiences and lessons on how to develop

international collaboration of clusters. According to Hausman et al., (2009, pp. 49–51), an

important lesson is that building trust, which is a prerequisite for cooperation, takes time as

people get to know each other. It is worthwhile to realize that there is no one universal

method for stimulating transnational cluster cooperation, since it can take place in very

different ways. In order to sustain collaborative activities, it is important to identify

quick-wins, whereas seeking a longer term commitment, which should involve several

important stakeholder groups, such as firms, research institutions and the public sector.

A crucial factor is efficient internal and external communication, which was proved by

the pilot programme focused on the ICT sector, in the framework of which easily acces-

sible communication tools played a role of an important platform for international cluster

cooperation. The overall outcome of the programmes shows that transnational cluster

cooperation creates added value and is beneficial to participating enterprises.

Conclusions

The primary aim of this paper has been to analyse if cluster initiatives, understood as some

formalized actions undertaken by regional actors, are overlapping with clusters, under-

Table 4. PIC in the framework of BSR InnoNet

Sector

ICT (entirevalue chain of

mobile devices)

Biotechnology withfocus on

environment FoodWood production

and furniture

Focus Value-chainclusters

Emerging clusterswith research-intensiveactivities

Mature clusters intraditionalsectors

New businessopportunitiesfor matureindustries

Partner fromPoland

ICT WestPomeranianCluster

Baltic Eco-EnergyCluster

A group of Polishfirms representedby WarsawUniversity ofLife Sciences

WielkopolskaFurnitureCluster

Otherparticipatingcountries

Denmark Finland Finland FinlandFinland Norway Iceland LatviaLatvia Sweden Sweden LithuaniaSweden Sweden

Source: Own compilation.


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stood as real concentrations of economic activity in specific industries. Since clustering is

believed to be a driving force for innovativeness and competitiveness at the enterprise and

regional level, it has become an important element of different policy actions. In Poland,

the cluster-based policy is becoming a popular concept, because supporting growth poles

for the economy may be an efficient way to achieve endogenous growth. It is evidenced in

this study by presenting the instruments stimulating the creation of cluster initiatives,

which positively verify hypothesis 1. However, high availability of public financial

funds for clustering may result in the emergence of many cluster initiatives that do not

meet the criteria characterizing real cluster structures, especially geographical concen-

tration of firms specializing in a specific sector or inter-related sectors. One of the chal-

lenges for this research, dealing with the ambiguity of the cluster concept, failing to

define proper boundaries for cluster structures, both geographical and industrial, was

tackled by conducting an analysis for LQs for existing ICT cluster initiatives on three geo-

graphical levels in Poland, starting from the biggest: NUTS 2 (voivodships), NUTS 3 (sub-

regions) or NUTS 4 (powiats, sometimes corresponding to bigger cities). When setting

proper boundaries for the ICT sector, we decided to take into account the following div-

isions distinguished under NACE Rev. 2 classification: manufacture of computer,

electronic and optical products, publishing, including software publishing, telecommuni-

cations, computer programming, consultancy and related activities, and information

service activities. The results of the research show that not all ICT cluster initiatives in

Poland represent real concentrations of ICT-related NACE divisions, which partly con-

firms hypothesis 2. However, there is a visible pattern that the LQs are higher for

smaller geographic areas (NUTS 4 level), which usually represent big cities, like

Warsaw, Cracow, Wroclaw, Gdansk or Poznan, being a core of cluster initiatives,

which confirms hypothesis 3. In particular, analyses of the LQs for 2 indicators: employ-

ment and the number of firms show that there are high concentrations of ICT-related

activities for the following formally established cluster initiatives:

. Wielkopolska ICT Cluster, but mostly at the local NUTS 4 level (Poznan city),

. ICT Pomerania Cluster, especially at the NUTS 3 level (Tricity subregion) and NUTS 4

(Gdansk and Gdynia cities),. ICT clusters in Mazovia voivodship (NUTS 2) in general and Warsaw, the capital city of

Poland, in particular with neighbouring powiats (NUTS 4),. ICT cluster in Malopolskie voivodship, but mostly at the local NUTS 4 level (Cracow,

the second biggest city in Poland),. Cluster “Knowledge and Innovation Community for ICT” in Lower Silesia voivodship,

especially at the local NUTS 4 level (Wroclaw city),. Eastern Poland Informatics Cluster at NUTS 4—Rzeszow city, where its central office is

located.

The pattern for LQs for data on firms’ incomes, when comparing to employment and the

number of firms, is different since firms’ incomes in ICT-related activities are even more

concentrated in Mazovia voivodship, particularly in its capital—Warsaw, and to a lesser

extent in Cracow, the second biggest Polish city. It may be explained by the fact many

companies report their earnings at their main seats, which are usually located in big

cities, whereas production facilities and employment may be spread throughout the

whole region or a country. The analysis of high concentrations of ICT-related activities


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helps also to identify some potential ICT cluster initiatives that may be developed in some

regions that have been lacking such formal initiatives until now, like Lodz city. This

finding also shows that clusters and cluster initiatives are not the same phenomena and

that there may be even a situation where real cluster exists in a specific region but no

formal cluster initiatives has been developed.

The evidence from some activities undertaken in Poland with the objective to support

internationalization of cluster initiatives suggests that this is an important stage of cluster-

ing processes, enhanced by globalization and fast development of ICT, which confirms

hypothesis 4. Experiences from PIC in the framework of the BSR InnoNet programme

show that transborder collaboration of cluster initiatives influences member firms’ inter-

nationalization behaviour, encouraging it to establish business relations in foreign

markets. However, developing mature interactions with foreign partners is a long-term

process and there are many influencing factors, like trust-building, permanent commitment

involving several important stakeholder groups, or efficient systems of internal and exter-

nal communication. The value added from establishing formalized cluster initiatives is

that they create institutional framework encouraging the formation of these elements, con-

tributing to international cooperation.

To conclude, the study shows that there is a visible trend towards clustering in Poland,

reflected both in entrepreneurs activities and public policy instruments. Sometimes, emer-

ging cluster initiatives are not fully bottom–up initiatives and they do not meet the criteria

of the theoretical model of clusters, because geographic concentration and specialization is

lacking. However, most of the identified cluster initiatives overlap with real clusters.

Therefore, public support for well-developed cluster initiatives helps to concentrate

resources on locations with high-economic potential, contributing to the emergence of

growth poles for the whole economy. Moreover, the cluster policy seems to be effective

even when different instruments are directed to smaller cluster initiatives, not representing

real regional specialization, because it may help to stimulate horizontal cooperation in the

economy, as well as to increase the growth potential of embryonic clusters and transform

them into mature structures, but this is a long-term process.

Note

1. Arkadiusz Kowalski, co-author of this article, was representing Poland in PIC in the framework of BSR

InnoNet.

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clusters versus cluster initiatives, with focus on the ict sector in poland

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