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Citation: Wojciech Dyba, Tadeusz Stryjakiewicz & Valentina De Marchi (2020)
Knowledge sourcing and cluster life cycle – a comparative study of furniture clusters in Italy and
Poland, European Planning Studies, 28:10, 1979-1998, DOI: 10.1080/09654313.2019.1701996
Knowledge sourcing and cluster life cycle – a comparative study of
furniture clusters in Italy and Poland
Abstract: Clusters are places that facilitate knowledge sharing and dissemination among firms
and institutions working in functionally-related fields. Structural changes that take place
within clusters over time influence knowledge-related processes and require new approaches
towards external knowledge sourcing. In this paper we use a mixed-method approach to
investigate different knowledge sources that firms use at different stages of a cluster life
cycle. The empirical research comprises the investigation of two clusters that specialise in the
same kind of economic activity, i.e. in furniture industry, but are at different stages of their
life cycle. These are, a mature cluster – the Livenza district in Italy and a growing one – the
Kępno cluster in Poland. The analysis revealed that firms in a mature cluster use a greater
variety of external knowledge sources and more knowledge intensive sources than those in
growing clusters do. This may be explained by more homogeneous and well-established
knowledge pools at later stages of a cluster life cycle and/or by higher competition between
firms offering similar products.
Key words: knowledge sourcing, clusters, cluster life cycle, industrial districts, furniture
industry
1. Introduction
The constant creation or acquisition of new knowledge is vital for firms’ growth and their
ability to adapt to changing market conditions (Davenport & Prusak, 1998; Probst, Raub &
Romhardt, 2000). Knowledge sourcing possibilities, however, are not the same everywhere;,
certain places create special conditions for searching for and obtaining external knowledge.
Industrial districts or clusters
1
– agglomerations, in relatively small areas, of a high number of
companies specialized in functionally-related fields and interacting among each other – have
been investigated as elective places for knowledge sharing and disseminating across
companies and institutions (Bathelt, Malmberg & Maskell, 2004). The more clusters are
challenged to keep up with globalisation dynamics (as evidenced by De Marchi, Gereffi &
1
The term industrial districts dates back to Marshall (1890) and was popularised in the 1990s in reference to the
“Third Italy” area, i.e. the regions in north-eastern part of Italy, characterised by many prosperous small and
medium-sized firms in traditional industries (Becattini, Bellandi & De Propris, 2009; Pyke, Becattini &
Sengenberger, 1990), whereas the concept of cluster is rather associated with the studies by Micheal Porter
(Porter, 1990). Although there is an ongoing debate on the subtle differences between the two terms (De Marchi
& Grandinetti, 2014; Ortega-Colomer, Molina-Morales & de Lucio, 2016; Porter & Ketels, 2009, in this paper
we are going to use the term cluster and industrial districts are synonymous.
2
Grandinetti, 2018; Rabellotti, Carabelli & Hirsch, 2009), the more important it is to
investigate knowledge sourcing activities that might spur their development.
A number of studies in the management literature have focused on external sources of new
knowledge and on their time-varying feature using the firm as the level of analysis (Huggins,
Izushi, Prokop & Thompson, 2015; Kang & Kang, 2009; Zollo & Winter, 2002). In the
context of clusters, the literature suggested that additional, relevant knowledge sources are
available to firms (Camuffo & Grandinetti, 2011), making it a peculiar context of analysis to
understand knowledge dynamics. However, the evolutionary dimension of knowledge
sourcing in clusters has been rather overlooked. Additionally, the few studies that have
investigated how knowledge pools and knowledge networks change during a cluster evolution
(Bergman, 2008; Menzel & Fornahl, 2009; Stough, 2015), have mostly focused on just one
life cycle stage at time. Against this background, the novelty of this study lies in the
comparison of knowledge sourcing patterns of firms operating in two clusters that are at
different life cycle stages – comparing a growing cluster with a mature one – yet having
similar structural characteristics and industry specialization. The adoption of a mixed-method
approach allows not only to quantify the extent to which different sources are adopted across
stages, but also to investigate in more details the rationale for these differences. Such an
analysis contributes both to the literature interested in investigating the evolution of industrial
districts (e.g., De Marchi et al., 2018, Belussi et al 2018) – by providing additional insights
regarding how firms access external knowledge considering for different life cycle stages –
and to the literature focusing on knowledge sourcing in clusters (e.g., Camuffo & Grandinetti,
2011), suggesting the importance to consider the specific life cycle stage in which the cluster
is in.
2. Knowledge sourcing in clusters
A wide range of studies explored firms’ knowledge sourcing, focusing especially on how
external actors, such as suppliers, customers and KIBS (knowledge intensive business
services) provide firms with novel knowledge that can be used in the company,
complementing their internal knowledge base (Huggins et al., 2015, Kang, Kang, 2009,
Robertson & Gatignon, 1998). Clusters, understood as geographic agglomerations of
companies (manufacturing firms, but also their specialised suppliers or service providers) and
associated institutions related to each other and operating in a particular business area, are
believed to create special possibilities for external knowledge sourcing, that is for obtaining
3
both payable and openly available knowledge (Porter, 1990; 1998, Camuffo & Grandinetti
2011). Proximity of firms and industries, combined with their similar specialisation (focus of
activity) in clusters creates possibilities for both intentional and unplanned knowledge
transfer, diffusion and spillovers (Dahl & Petersen, 2004; Maskell & Malmberg, 1999;
Malmberg & Maskell, 2002; Porter, 1998; Saxenian, 1994; Storper & Venables, 2004;
Swann, 2009). Indeed, the intensity of the knowledge flows within clusters is fuelled by the
proximity among cluster actors. This proximity is not only geographical, but also cognitive,
organisational, social and institutional (Boschma, 2005, Lazzaretti, Capone 2016). The extent
to which geographical proximity matters for knowledge flows in clusters may depend on the
type of the knowledge base – analytical, synthetic or symbolic – of the cluster (Martin,
Moodysson, 2013).
Knowledge sourcing and learning in the cluster context can take different forms. According to
Menzel and Fornahl (2009), knowledge exchange in clusters takes place through direct
interaction, monitoring other firms (and combining their knowledge with the firm’s own),
social contacts or labour mobility. Similarly, in their review of the literature on that topic,
Camuffo and Grandinetti (2011) identified diverse mechanisms of inter-firm knowledge
transfer: inter-organisational and interpersonal relations, observations aimed at the imitation
of others, the mobility of human resources from one existing firm to another, and the creation
of new ventures through spin-offs (i.e. the mobility of human resources from one existing
firm to a newly born firm).
In their model of knowledge generation in clusters, Bathelt, Malmberg and Maskell (2004)
state that firms that share values, attributes and ways of conduct create conditions for a ‘local
buzz’, an informal knowledge exchange, and for ‘global pipelines’, connections to knowledge
sources beyond the cluster (reaching outside the region in which it operates). Indeed,
conditions that favour knowledge development in clusters not only stem from knowledge
acquired from agents operating inside those clusters, but also come from outside the cluster
(Giuliani et al., 2005). External-to-the-clusters relations with suppliers, customers, research
and marketing institutes (the so-called KIBS) may significantly improve the capabilities of
cluster firms (De Marchi, Di Maria & Gereffi, 2018; Nadvi & Halder, 2005). The pool of
knowledge stemming from the exploitation of both local and distant knowledge structures
makes clusters learning systems and leads to the benefit of cluster firms and of the area in
which they operate (Belussi & Sedita, 2012; Tödtling, Asheim & Boschma, 2013). Access to
external knowledge sources significantly increased at the end of the 20th century as
globalisation and the rapid development of ICTs brought new possibilities for knowledge
4
sourcing online. It allows firms search for and contact external firms, including KIBS, and
provides wide access to the media in terms of branch press or portals (Bellandi & De Propris,
2015).
Taking the above into consideration, external knowledge sources of firms in clusters may be
divided into three groups: (1) traditional mechanisms of inter-firm knowledge transfer in
clusters, (2) knowledge acquisition from knowledge-intensive organisations and (3) media
and events. Group (1) has strict relations to cluster advantages and includes, collaboration
with similar firms located nearby or local suppliers, informal talks, imitation of similar
surrounding firms or acquiring new workers. Group (2) includes institutions and business
environment firms acting as external knowledge sources for cluster firms. They provide
opportunities for professional knowledge acquisition, by offering innovative products or
services, experts’ reports or trainings and common collaboration projects. Finally, group (3)
comprises the media, branch press, magazines and internet search engines or websites as well
as participation in professional events.
3. Knowledge sourcing and cluster life cycle
Clusters and their characteristics change as they go through the life cycle stages: emergence
(existence), growth (expansion) and maturity (also called exhaustion or sustainment) and
finally lock-in/decline or rejuvenation and transformation/renaissance (Bergman, 2008;
Maskell & Kebir, 2005; Belussi & Hervás-Oliver, 2015). Clusters evolve and go through the
subsequent stages of the cluster life cycle due to exogenous (mostly industry-driven) or
endogenous (cluster-specific) factors (Lorenzen, 2005; Maskell & Malmberg, 2007). As for
the endogenous factors, along the cluster life cycle, cluster agents increasingly utilise the
potential of the cluster as their perception of the cluster improves and the capacity for
collective action grows, and the cluster’s diversity increases as opportunities for the
exploitation of synergies, networks and value chains arise and multiply. Several recent studies
are suggesting the life cycle is a key feature of clusters to understand their functioning
(Belussi, 2018; Fornahl, Hassink & Menzel, 2015; Trippl, Grillitsch, Isaksen & Sinozic,
2015). However it is sometimes tricky to identify and measure stages of cluster life cycle:
other authors point out that clusters are constantly adapting to external conditions; that their
development trajectories are complex and multi-fold, connected with path dependence and
influenced by the combinatorial variety of different, evolutionary growth factors (Belussi,
2009; Belussi & Sedita, 2009; Martin & Sunley, 2011). Interestingly for the sake of this
5
study, it is clear however that such stages are connected with the knowledge strategies of
firms, despite such a connection have been barely the focus of the literature. Indeed, the
knowledge pool is suggested to change along the different stages of the cluster life cycle.
Processes involving knowledge sharing or disseminating, known also as knowledge
spillovers, diffusion or transfer appear in clusters only after some time, when trust and
networks are built between cluster agents (Krugman, 1991, Iammarino & McCann, 2006,
Karlsson & Gråsjö, 2014). In the emerging stage, knowledge is heterogeneous, often tacit,
concentrated within a few leading firms. A growing stock of available knowledge and
competences allows the growth of the cluster, represented by a growing number of firms and
supportive institutions. During the growing stage, the geographical proximity facilitates
informal interactions, cooperative networks, spin-offs and labour mobility, which are the
ways of knowledge flow (Boschma & Ter Wal, 2011; Camuffo & Grandinetti, 2011). The
growth of a cluster is characterised by knowledge sharing and external knowledge inflows,
which expand local knowledge pools (Li, 2018). Its growth is possible thanks to constant
knowledge exploration and exploitation activities (Gancarczyk, 2015). At the same time,
knowledge diffusion may remain selective in this stage, because it depends on the firm-
specific characteristics (as in the case of the wine industry in Chile, investigated by Giuliani,
2007) as well as on social and cognitive proximities between cluster agents, which are
sometimes more important than geographical proximity (as proved in the case of the footwear
cluster in Italy by Boschma & Ter Wal, 2007). In the expansion stage, the changing
institutional setting, especially the growing science knowledge-base, allows firms to
cooperate with universities and research centres. Various networks of formal and informal
cooperation grow in number and density (Bergman, 2008; Stough, 2015). Mature clusters
have knowledge pools that are homogeneous and accessible to all cluster stakeholders, as
highlighted in a study on the evolution of the Korean shipbuilding cluster by Stough (2015).
Finally, a too specific knowledge base and a too high focus on specific markets and
technologies, together with closed networks of actors, impede cluster adaptability and may
finally lead to a lock-in and decline (Hassink, 2015; Stryjakiewicz, 1999). Therefore, it seems
that it is of vital importance for firms in the maturity stage of a cluster life cycle to go beyond
simple local knowledge transfer via information sharing and to create channels of external
knowledge acquisition (Bathelt et al., 2004; Camuffo & Grandinetti, 2011). Bathelt and
Cohendet (2014) add that mechanisms and conditions of combining local knowledge creation
and development and creating connections with knowledge located elsewhere are still
6
insufficiently known. Table 1 summarizes the discussion reported, considering for each of the
four cluster’s life cycle stages.
All in all, the literature suggests that knowledge pools, actors and networks change in clusters
along the cluster life cycle stages in a way that should affect the knowledge sourcing
strategies of cluster firms. Accordingly, we formulate hypothesis 1 as
HP1, The relevance of external knowledge sourcing for cluster firms varies depending
on the stages of the cluster life cycle.
Table 1. Knowledge in different cluster life cycle stages
Emergence
Growth
Maturity
Decline
Institutional
endowment,
actors of
knowledge
flows
No branch
institutions,
pioneering firms
act as
knowledge
creators and
disseminators
First supportive
institutions,
successful spin-
offs, old and new
firms start
engaging in
knowledge
exchange
Active role of
research
organisations,
universities,
branch
institutions; role
of knowledge
gatekeepers
Institutions are
not providing
growth
incentives and
impulses for
firms,
the number of
cluster firms
(including those
engaging in
knowledge
creation and
flows) decreases
Heterogeneity
of knowledge
Heterogeneous
knowledge base
Stock of available
knowledge is
growing and is
more and more
utilised by actors
(due to their
growing
absorptive
capacity)
Well-
established
knowledge
pools, focused
competences,
homogeneity
Too specific
knowledge base,
exhausted
technology paths
Knowledge
networks
between
firms and
institutions
Rare, not stable
Increasing in
number and
density
Common,
include non-
local agents
Closed or
exploited
networks impede
adaptability of
the cluster, no
new external
knowledge
channels
Source, Own elaboration based on Bergman (2008), Menzel and Fornahl (2009) and Stough (2015).
7
A specific reasoning within this context regard the maturity stage of cluster life cycle.
According to Menzel and Fornahl (2009), changes in the heterogeneity of knowledge are
triggering the dynamics of a cluster, suggesting that the more mature and larger the cluster (in
terms of the number of organisations, actors, employees etc.), the more diverse in terms of
knowledge, competences and organisational forms. In particular, the more mature the cluster
is, and the more firms compete for the same market share, the more precious an asset the
firm’s knowledge is – it is a secret that is not to be shared with others, as it represents their
biggest competitive advantage. While successful clusters are often characterised by a
coopetition, that is, an optimal balance between cooperation and competition (You &
Wilkinson, 1994), in mature clusters, competition becomes much fiercer (Staber, 1998).
Extending a reasoning developed for the industry level by Audretsch and Feldman (1996), it
can be supposed that positive agglomeration effects during the early stages of the cluster life
cycle are replaced by congestion effects during its later stages. The clustering advantages may
turn into disadvantages as the clustered companies become locked into a trajectory that once
marked their success but is no longer able to cope with contemporary development (Pouder &
St. John, 1996). Therefore, in a more competitive environment (in the late stage of a cluster
life cycle), firms are less willing to reveal their knowledge to other firms because they might
lose their competitive advantage of knowledge value (Schrader, 1991). Formal and informal
contacts between firms as well as employees moving between companies are widely
recognised sources of new knowledge in clusters (Camuffo & Grandinetti, 2011). So mature
clusters, characterised by homogeneous knowledge pools and high and intensifying
competition, mean that firms need to seek diverse, harder-to-acquire knowledge sources to
develop the products and processes on which they can base their competitive advantage. Such
sources might be represented by cooperation with knowledge intensive partners (rather than
with business partners), including consultants (or knowledge intensive business services –
KIBS), universities, research or technology centres (Bathelt et al., 2004; De Marchi &
Grandinetti, 2012). Moreover, only after some time will firms in mature clusters have the
knowledge absorptive capacity or ability to exploit professional, external knowledge
(Boschma & Iammarino, 2009; Hervas-Oliver et al., 2012). Cluster firms investing in
achieving that knowledge rather than just capitalising on knowledge in the ‘district
atmosphere’ need to engage in touch with representatives of universities and research centres
(Jenkins & Tallman, 2010). Only engagement in purposeful relations may lead to innovations
and competitiveness (Fitjar & Rodrigues-Pose, 2011), so that, in this context, the proximity
with those partners might be less relevant. That is to say: while proximity matters also in the
8
context of firms – knowledge intensive organizations (see e.g. Messeni Petruzzelli, A., 2008,
Muscio, Quaglione, Scarpinato, 2012) firms might engage also with KIBS and universities
being located outside the district, as soon as they have the specialist knowledge needed (Di
Maria, Bettiol, De Marchi, Grandinetti, 2012; Labory 2011). Accordingly, we formulate
hypothesis 2 as
HP2, The more mature the cluster, the more likely firms are sourcing knowledge from
knowledge-intensive organisations.
4. Investigating knowledge sourcing in clusters, methodology
To investigate the extent knowledge sourcing of cluster firms changes according to the stage
of the cluster life cycle and the relevance of professional knowledge sources in the maturity
stage, we pursued mixed-method research, studying two clusters similar in size and
specialisation, but at different life cycle stages.
In the cluster literature, comparative studies have proved effective in showing mechanisms
driving growth and in developing and testing new ideas (e.g. Dyba, 2016; Li, 2018; Mossig &
Schieber, 2016; Parker, 2010; Saxenian, 1994). We focused on clusters specialising in the
same economic activity, i.e. in furniture industry, hence representing the low-tech
specialisation that characterises many clusters in the European context. The analysed clusters
are located in two of the most important countries for manufacturing furniture, Italy (ranking
4th for furniture production and 3rd for exports worldwide as of 2017) and Poland (ranking 6th
and 4th respectively).
2
We selected the most important furniture clusters within those
countries, the Livenza furniture cluster, located at the border of the north Italian regions of
Veneto and Friuli Venezia Giulia and the Kępno furniture cluster in the Wielkopolska region
in Western Poland.
Our empirical research consisted of mixing both quantitative and qualitative methods. To
investigate basic characteristics, to verify the comparability of the two clusters and to get an
overview of the possible knowledge sources used by firms, interviews with sector analysts,
public institutions’ representatives and scholars were conducted. Importantly, the authors had
been previously involved in several projects on clusters; this gave them a basic understanding
of how clusters function in the regions selected for the study. The second step was to
distribute a survey questionnaire to a sample of the Italian and Polish furniture firms to
understand the different knowledge sourcing mechanisms across the two clusters. Finally, in-
2
Data retrieved from Eurostat (https,//ec.europa.eu).
9
depth, structured interviews with selected companies from both clusters (three from each
cluster) were conducted to get a deeper comprehension of the results emerging from the
quantitative analysis.
4.1. Step 1, preliminary investigation
The interviews took place in the first quarter of 2017 (Table 2). They aimed at gathering basic
information on both clusters, understanding the key dynamics taking place within the districts
as well as confirming and complementing the list of the most common knowledge sources
used by the furniture firms.
Table 2. Information on interviews during Step 1
Cluster
Interviewee
Place and duration
Livenza
Director, Chamber of Commerce of Treviso-Belluno
Treviso, 1,05
Professor, University of Padova
Padova, 1,30
Professor, Ca’Foscari University of Venice
Venice, 0,50
Kępno
Office Director, Polish Chamber of Furniture
Manufacturers
Warsaw, 1,45
Editor in Chief, “Business Furniture” Magazine (most
recognised branch press in Poland)
Poznań, 1,20
Researcher, Poznań University of Economics
Poznań, 0,40
Professor, Adam Mickiewicz University in Poznań
Poznań, 1,10
The Livenza furniture cluster dates back to the early 1900s, despite the industrial growth of
the clusters boomed between 1960s and 1990s (see Buciuni & Pisano, 2018). In 2017, the
Livenza furniture cluster consisted of 413 firms.
3
; number production levels and employees
reduced during the latest years – i.e. starting the mid 2000s – yet at a lower level than districts
specialized in the same sector and in the same area (see Buciuni & Pisano, 2018, De Marchi
& Grandinetti, 2014). It is composed mostly by small and medium-sized firms, many of them
family-owned, and holds a recognised position worldwide because of the firms’ design
capabilities (Buciuni, Corò & Micelli, 2014). However, just a part of the local companies are
original brand manufacturers (OBMs) selling their branded products in international markets.
A high level of firm stage-specialisation exists, and several companies work as original
equipment manufacturer (OEMs) for large multinationals such as IKEA (De Marchi Di Maria
& Micelli, 2013). Despite the steep reduction in the number of firms and employment
following the recession, several contributions testify to its resiliency and dynamics (Buciuni
3
Data on firms in Livenza cluster were received from the Chamber of Commerce Treviso-Belluno.
10
& Breznitz, 2014; De Marchi & Grandinetti, 2012), so it can be defined as a mature rather
than declining cluster.
In 2017, the Kępno furniture cluster consisted of around 380 firms; it was the largest
concentration of furniture companies in Poland (Dyba, 2017a, 2017b)
4
. The birth of the
cluster dates back to 1990s, when there has been an important increase in the number of firms
and the first local institutions have been founded; the cluster is still characterised by a
moderate level of social capital (Stryjakiewicz, 1999). The cluster has been classified in the
literature as being in a growing stage, because of i) the growing number of active firms, ii) the
recent foundations of supporting institutions, iii) the raising cooperation with business
environment organisations and recent innovative activity (Dyba, 2016). The cluster have been
recently recognized for its role also at the public level, so that public funding has been
recently devoted to support cluster development and competitiveness (Ratajczak-Mrozek &
Herbeć, 2013; Jankowska & Pietrzykowski, 2013). Kępno’s firms are rarely known outside
Poland, and many firms, especially the largest one, work as OEMs for other brands, including
IKEA and other multinationals. There are, however, also a number of smaller companies
being Original Design Manufacturers (ODM) as they design and produce furniture that are
then rebranded and sold both in large furniture outlets or to other companies. Some are
Original Brand Manufacturers (OBM), which are mostly working for the local market – even
when exporting, the share of sales from foreign markets then to be low (Hryniewiecki, 2018).
4.2. Step 2, The survey
The survey was administered in the first, second and third quarters of 2017. Participants were
asked to fill in an online questionnaire. Two specialised companies located close to the
clusters were engaged to spur company participation. The survey targeted top managers or
CEOs. The final sample consists of 213 firms in Livenza and 100 in Kępno, respectively
around ½ and ¼ of the total populations.
5
The survey included questions on knowledge
4
Data on firms in Poland were received from Emmu B2B. Public statistics in Poland are not precise, and regional chambers of commerce do
not gather detailed data on firms. Therefore, the database on furniture firms is obtained from a private company specialising in business
statistics and it is only approximate.
5
It must be noted that it was difficult to encourage entrepreneurs from Kępno to participate in this research. It
was prepared by a public university representative, conducted by a local firm, with the letter of recommendation
from the regional chamber of commerce and the assertion that the answers will be processed anonymously.
However, most companies replied that they do not cooperate with universities because they do not have time or
do not see the purpose of such surveys. Some were suspicious about the real aim of such analysis. It proves that
firm-university cooperation in Poland is still difficult. 100 answers (a minimum number of answers before the
research) must be treated as a large number.
11
sourcing, asking respondents to assess, on a scale of 1 (totally unhelpful) to 5 (definitely
useful), the usefulness of 14 types of knowledge sources for their current activity.
The data collected were then analysed by the mean of multivariate data analysis to assess if
there are statistically significant differences between the clusters in Italy and Poland.
The basic characteristics of firms that took part in the survey are presented in Table 3. The
sample of the two clusters show similar export strategies, as for countries involved, yet a
smaller propension toward foreign markets. Furthermore, they are both characterized by a
prevalence of small companies, despite the share of medium and large firms is bigger in Italy.
A key difference regards the average year of foundation, confirming the fact that the two
clusters are in a different life stage cycle.
Table 3. Characteristics of the firms that took part in the survey
Livenza
Kępno
Sample
n=213
n=100
Set up year of
firms
Before 1990 – 47%
1990-1999 – 25%
2000-2009 – 15%
2010-2017 – 13%
Before 1990 – 6%
1990-1999 – 21%
2000-2009 – 34%
2010-2017 – 39%
Size of firms
(number of
employees)
Small (1-24), 117 (54,9%)
Medium (25-99), 81 (38%)
Large (>100), 15 (7%)
Small (1-24), 75 (75%)
Medium (25-99), 21 (21%)
Large (>100), 4 (4%)
Percent of firms
exporting their
products and top
five export
destinations
62%
France, Germany, Russia, Great
Britain, Switzerland
45%
Germany, Great Britain, France,
Italy, Russia
Share of firms
selling to the
final market
(OBMs)
45%
53%
Important
cooperating
institutions (with
foundation year)
Chamber of Commerce Treviso-
Belluno & Chamber of Commerce
Pordenone (since the 1960s)
ASDI – Agenzia per lo Sviluppo
del Distretto Industriale del Mobile
del Livenza (2002)
Wielkopolska Chamber of
Commerce (1989),
Poznan University of Life
Sciences – Laboratory of
Furniture Research (since 2000),
ITA sp. z o.o., sp. k. (1999),
TÜV Rheinland Polska sp. z o.o.
(1997) – certification bodies
4.3 Step 3, In-depth qualitative interviews
Finally, in the second quarter of 2018, the founders or current owners of six companies, three
in each cluster, were invited to participate in in-depth interviews. The interviews aimed at
12
describing the knowledge sourcing strategies throughout the firms’ (and clusters’) evolution,
both referring to the single firms and the cluster as a whole. They also investigated how and to
what extent the location within a cluster helped firms to shape their knowledge sourcing
processes.
The choice of case studies was purposeful in that it focused on successful firms (i.e. those that
increased sales and profits in the last 10 years) representing different size groups yet offering
a similar type of product and having a leadership position in the cluster (as emerged in the
preliminary interviews). The characteristics of the selected firms are presented in Table 4.
Table 4. Data on selected firms and interviewees – Livenza (F1-F3) and Kępno (F4-F6).
Feature
F1-L
F2-L
F3-L
F4-K
F5-K
F6-K
Establishmen
t year
1953
1954
1962
1975 –
craft
workshop
2003 –
firm
1985–
craft
workshop
1990 –
firm
1992
Size group
Large
Medium
Medium
Medium
Large
Medium
Employees
(2018)
333
40
90
240
700
70-80
Turnover
(2017, mln
EUR)
65
9
10
14
25
8
Export
orientation
(% of
turnover)
25
5
35
95
40
60
Interviewee
CEO
CEO
CEO;
long-term
employee
Founder &
CEO
Founder &
CEO
Founder
& CEO
Interview
duration
1,01
0,51
1,33
1,38
0,59
0,46
The firm representatives were asked to report on the history of the firm, indicate strategies or
actions connected with knowledge sourcing. Additionally, they were asked about successful
and unsuccessful knowledge sourcing strategies regarding other firms in the cluster. The
stories of chosen flagship case studies and other meaningful examples of cluster firms aimed
at increasing understanding of the relevance and forms of knowledge sourcing in clusters at
different life cycle stages (Yin, 1994; Eisenhardt & Graebner, 2007).
5. Knowledge sourcing in clusters at different life cycle stages, the results of a
quantitative study
13
The results of our survey allow to distinguish most relevant categories of the knowledge
sources used by firms in the Livenza and Kępno furniture clusters (Tab. 5). They have been
divided into the same groups that emerge from the literature (described earlier).
14
Table 5. Most common categories of knowledge sources used by firms in the Livenza and Kępno clusters
Group
No.
Category
Livenza
Kępno
Difference
between the
two clusters
(ANOVA)
M
St.D.
Md
D
Rank
M
St.D.
Md
D
Rank
Traditional
mechanisms
of inter-firm
knowledge
transfer in
clusters
X1
Collaboration with similar firms located nearby
(fusions, alliances, agreements) and with suppliers
2,29
1,44
2
1
10.
2,96
1,51
3
1
3.
***
X2
Talks or informal contacts with stakeholders
2,12
1,23
2
1
12.
2,26
1,39
2
1
5.
X3
Comparisons or imitation of other cluster firms
3,30
1,27
4
4
1.
2,97
1,40
3
3
2.
**
X4
New workers
2,59
1,38
3
1
6.
1,78
1,08
1
1
9.
***
Knowledge
acquisition
from
knowledge-
intensive
organisations
X5
Consulting, expert's reports and training of external
firms
2,80
1,33
3
4
4.
1,39
0,93
1
1
12.
***
X6
Offers sent or presented by sales representatives of
external firms
2,15
1,20
2
1
11.
2,14
1,25
2
1
8.
X7
Collaboration with universities and research centres
1,77
1,19
1
1
13.
1,20
0,73
1
1
13.
***
X8
Trainings and events of industry chambers, regional
development agencies or other public organisations
2,29
1,33
2
1
8.
1,45
1,02
1
1
10.
***
X9
Non-profit organisations, e.g. associations, branch
institutions
2,20
1,33
2
1
9.
1,50
1,02
1
1
11.
**
X10
Clients (single opinions, satisfaction surveys)
2,79
1,41
3
1
5.
2,18
1,34
2
1
7.
**
Media and
events
X11
Participation in fairs, exhibitions or branch events
3,24
1,50
4
4
2.
2,57
1,55
2
1
4.
***
X12
Branch press, magazines, leaflets, books
2,43
1,28
2
1
7.
2,22
1,32
2
1
6.
X13
Internet search engines and websites
3,14
1,37
3
4
3.
3,55
1,49
4
5
1.
**
Legend, M – mean, St.D. – Standard deviation, Md – Median, D – Dominant, R – Rank in cluster (from 1., the most used source in that cluster among the 14 sources listed, to 14., the
least one)
Statistical analysis of variance (ANOVA), *** α = 0,01 ** α = 0,05
15
The evidence reported in Table 5 allows to address both the research hypotheses postulated in
this article. A part for the relevance of internally developed knowledge, which emerges as an
important source of new ideas and project in both the cluster analysed, the analysis reported
indeed support the hypothesis that knowledge sourcing strategies varies across different life
cycle stages, even when focusing on cluster specialized in the same industry. Generally
speaking, the average values associated by firms to the source of knowledge listed in the
survey (on a 1 to 5 Likert scale) is statistically different considering for almost all the sources,
apart for the ‘Talks or informal contacts with stakeholders’ (X2), the ‘Offers sent or presented
by sales representatives of external firms’ (X6) and ‘Branch press, magazines, leaflets, books’
(X12) – sources that have a similar importance in both clusters. Mean importance of 8 out of
13 external sources listed in the survey was rated higher by firm representatives in the mature
cluster (Livenza) than in the growing cluster (Kepno). In Livenza, five sources had a
dominant value of 5 or 4, and the overall median value (for all categories) was 3; in Kępno,
only two sources had a dominant value of 5 or 4, and the overall median was 2. Moreover, in
Livenza firms use on average 6,55 external knowledge sources (calculated as the mean value
of the number of sources rated as 3 or higher),while in Kępno only 5,53. This suggests that
the Livenza firms use more diversified knowledge sources.
Therefore, the results seem to support our hypothesis 1 – that the relevance of external
knowledge sourcing is higher in mature clusters; firms based in mature clusters are more
likely to draw knowledge from a broader set of external sources
Furthermore, results are in line with the expectations postulated in Hypothesis 2, suggesting
the higher relevance of knowledge-intensive organisations for more mature clusters. Firms
based in the Livenza districts are indeed reporting, on average, higher values for all the
sources listed in this groups (from X5 to X10, as of Table 5). More specifically, within the
group “Knowledge acquisition from knowledge-intensive organisations”, the highest
differences in the usefulness of knowledge sources concern external consulting or trainings
(X5) and actions of public institutions (X8). Both categories are rated as important in Livenza
but not in Kępno. The mean usefulness of all knowledge sources from the group 2 –
knowledge acquisition from knowledge intensive partners in Livenza is 2,27 whereas in
Kępno only 1,63 (respectively: 43,7% and 19,83% of sources within this category was rated
3, 4 or 5 in Livenza and Kępno). This suggests that the business environment (institutional
setting) is more developed in the Italian case, creating more opportunities to use sophisticated
knowledge sources.
16
As for the other types of knowledge sources, high average usefulness was attributed in both
clusters to sources within the “media and events” group comprising participation in trade
fairs, exhibitions and branch events as well as newest source, the internet (X11 to X13). At
the same time, most knowledge sources in both clusters had a dominant value of 1, as most
respondents rated only a few knowledge sources (with others not used at all or used rarely).
Within the group “traditional mechanisms of knowledge transfer in clusters”, the most
frequent category of knowledge sourcing was “comparisons with and imitations of other
similar firms” (X3); that source was listed 1st in Livenza and 3rd in Kępno. While
collaboration with other firms and suppliers (X1) is perceived as moderately useful as a
knowledge source in Kępno (4th rank), it is not treated as such in Livenza. Talks and informal
contacts with similar firms (X2) are deemed not useful in both clusters. However, a
comparison with other firms ranks around average in Kępno; in Livenza it is one of the last.
This suggests firms are afraid of competitors and do not want to reveal their knowledge,
which is in keeping with the idea of an increase in competition in mature clusters.
6. Knowledge sourcing mechanisms, the results of a qualitative study
To better understand the relevance and nature of external knowledge sources for cluster firms
at different cluster life cycle stages, six in-depth interviews were conducted. The interviewees
explained their previous and current knowledge sourcing patterns and identified the most
important of them. They also referred to other surrounding firms to show advantages and
threats concerning knowledge sources (summarised in Table 6).
Table 6. Relevance of knowledge sources in the growth and maturity stages of the
cluster life cycle (in the light of interviews)
Group
Specific
sources
relevant in
Livenza
Specific sources
relevant in
Kępno
Characteristics and threats concerning
knowledge
Traditional
mechanisms
of inter-firm
knowledge
transfer in
clusters
(X3), (X4)
(X3)
- Competition encourages constant
knowledge sourcing
- Knowledge coming from imitating
may not be sufficient for a firm’s
long-time growth
- Knowledge brought to the firm by new
workers employed as managers is current
Knowledge
(X5), (X10)
-
- External knowledge (especially in the
17
acquisition
from
knowledge-
intensive
organisations
form of consultations or trainings) is
sometimes costly, but it is considered worth
paying for
Media
and events
(X11)
(X13)
- Knowledge accessible through the
internet is easy to find and acquire but not
useful for complex issues/solutions
- Participation in trade fairs is costly, and
knowledge sourcing is usually not the most
important reason for this participation
Legend, X3, X4 and the like refer to the categories listed in Table 5.
The interviews confirm that only a few firms in the growing Kępno cluster use the
knowledge-intensive organisations as a knowledge source. In the mature Livenza cluster,
however, they play a certain role, supporting HP2; external consultancy is used when there is
a need to make important decisions concerning, for example, the introduction of new
technology, a new marketing campaign or expansion into new markets. When deciding on
new machines for production, one interlocutor (F2-L) admits he looked for inspiration in the
branch press, recently on the internet, but finally ‘always consulted the decisions and signed
the deals with the same person [an external consultant] who sold me the first machine and was
the expert in the furniture industry’. Another interviewee (F3-L) states that ‘automation was
introduced into the firm thanks to the offers of external firms (…). We also ordered
consultations on the possible expansion directions as well as predicted client expectations
(…). I think some other firms in here do the same as the market is really difficult’. KIBS have
the newest and most professional knowledge or the ability to find and exploit it. Interviewees
F1-L and F2-L mentioned certain help in accessing new knowledge by the local industry
chamber, while interlocutors F4-K and F6-K stressed that furniture firms in Kępno do not
receive any kind of support or knowledge from public organisations. Using this knowledge
source is more possible in Livenza because the business environment is more developed here
than in Kępno. This confirms research hypotheses 2 that external knowledge sources,
especially originated from knowledge-intensive organisations, are more relevant for firms in
mature clusters.
As for the traditional mechanisms of inter-firm knowledge transfer in clusters,
interviews allowed to confirm another significant difference between the two case studies,
which was already emerging in the quantitative analysis: employing managers from outside
the firm is much more popular in the mature cluster than it is in the growing one. The new
18
workers act as ‘knowledge agents’, bringing their knowledge, skills and expertise to the firm
and helping them to make the best strategic decisions. Many of them had previously been
employed in other firms of the same cluster. According to the interviewees, sometimes it is
not easy to decide about employing someone with a different vision of the business that may
want to change the routines, concepts or known methods of utilising old knowledge. As one
respondent (F2-L) reported, ‘the source of knowledge that makes you move, I mean as a
company, are always the firm top managers (…) They keep us informed, they tell us what
works and what does not, they update us in every way (…). They are professionals, prepared
to teach, to solve problems’.
Concerning other traditional knowledge sources in clusters, interviewees from both
clusters confirmed that imitating firms in the vicinity is common, and it is the most important
‘clustering advantage’ for the firms in both the investigated furniture clusters, indeed the
result was significant just at the 5% in the quantitative analysis. However, this form of
knowledge sourcing is only successful in certain situations and not in the long run, as it does
not allow structural changes and innovations to be made by a firm. As reported by (F5-K),
‘small companies from Kępno used to copy patterns of our commode – and some are still
producing it today (…) But we were constantly changing our industrial design, working on
new, more interesting furniture. Others that were only emulating us did not see the necessity
to change. Perhaps that is why some companies (...) stayed in the same situation they were’.
According to the interviewees, informal knowledge exchange rarely occurs in either cluster –
only between a narrow group of colleagues in Kępno – and is not treated as an important
source of knowledge for firms. In both clusters, firms are rather afraid of formal cooperation
with similar firms because it could lead to loosing their competitive advantage. One speaker
(F2-L) explains, ‘the cooperation between furniture firms practically does not exist (there is
none that I know of); (…) it is not possible, at least with my generation’. A speaker in the
mature cluster (F1-L) admits that ‘everyone – especially in this area – works on his/her own,
does not share (knowledge, expertise) as they are afraid of simply letting it go (…). However,
our competition is seen as a stimulus, with the awareness that the growth of one firm does not
mean the misfortune of others’. One speaker in Kępno (F4-K) says, ‘Those local small firms,
I’ve noticed, are observing our production and hope to learn something. Perhaps most of
them will not start exporting to the scale we have and will not take our customers, so
sometimes I share with them what I know (…) At the same time, many furniture firms are
afraid of formal collaboration, they prefer to lose rather to see that others make profits’.
19
The interviews also revealed the importance of media and events (especially internet
and trade fairs) in both clusters. Interviewee (F6-K) states that ‘of course we use the internet
as the source, but sometimes it is just to find information about another knowledge source –
like an offer of a certain firm, suppliers etc.’ Another interviewee (F5-K) suggested that in
young firms in Kępno employed are young managers that treat the internet as a normal and
necessary source, often the first one. Interviewee (F3-L) suggests there are routines at Italian
companies concerning known sources and the internet is used only as a complementary
element. Firms also attribute importance to participation in fair trades. However, a minor
difference lies in their aims, in Kępno it is mainly to sell furniture and sign large-scale
customers to sign contracts with; for the firms in Livenza, it is also important for them to stay
recognisable and be seen as a reliable partner on the market.
7. Discussion and conclusions
The study aimed to investigate similarities and differences in knowledge sourcing in clusters
at different stages of their life cycle by the mean of an in-depth analysis of a mature and a
growing furniture clusters, contributing to the current debate on the evolution of clusters and
their future challenges (Lazzaretti et al, 2019).
The quantitative and qualitative results support that mature and the growing cluster are
characterized by different external knowledge sourcing patterns; firms based in mature
clusters rely on a larger portfolio of knowledge sources than those in growing clusters. The
interviewees confirmed that the available knowledge pools and competitive pressures are
greater in the mature cluster (in line with Bergman, 2008 and Stough, 2015), thus creating a
higher need for using diverse knowledge sources.
The second key result of the analysis is that knowledge-intensive partners are far more
important as knowledge sources in the mature cluster. Significant statistical difference was
observed for such sources as external consultancy (KIBS), collaboration with universities,
trainings and actions of public institutions and non-profit organisations. All these sources
were valued higher in Livenza (Italy) than they were in Kępno (Poland). The more advanced
the business environment, and possibly the more developed the public and private
organisations that might provide specific services for cluster firms, the more likely firms are
willing to source widely for knowledge, in line with Menzel & Fornahl (2015).
Another important difference between the knowledge sourcing strategies across the two
clusters analysed is also to the new workers as a source of novel knowledge. This is a
20
traditional cluster externality (Camuffo & Grandinetti, 2011), but may also be treated as a
knowledge-intensive source. In a situation where the competition for scarce market resources
is growing, managers bring to the firm new knowledge that may be an advantage over other
firms. This seems to be an important aspect raising the absorptive capacity of firms, that is,
the ability to recognise the value of external information, its assimilation and application for
commercial aims (Zahra & George, 2002). Such a result supports the importance of
incorporating more investigation into human agency, a research gap in the cluster life cycle
studies observed by Trippl et al. (2015).
Finally, our study testifies also the high and growing role of the internet in terms of acquiring
new knowledge by cluster firms; further supporting how clusters’ firms are transforming as a
result of ICT advancements (Bellandi & De Propris, 2015). However, in-depth interviews
suggest that staff see it as a way to look for basic information and to further knowledge to be
acquired by other sources, rather than a source that might suffice on its own. Different from
knowledge sources that are outside a cluster, additional knowledge sources can be accessed
inside it. Our study confirms that the cluster creates additional possibilities to use such
external knowledge sources as formal collaboration with other firms or informal contacts with
other stakeholders (especially in the growing cluster, as in Dyba, 2016), but not all companies
make use of it. This contributes to the literature reporting an increasing heterogeneity within
clusters (De Marchi et al., 2018). As stems from our research, talks and informal contacts with
firm representatives in both clusters are rare and not perceived as meaningful. However,
comparisons with other, similar, firms located in the cluster are common; imitation is valued
as a way of accessing technological and business information. The competition with
surrounding furniture producers is perceived as a stimulus to constantly change and develop.
All in all, this study makes important contributions to the literature focusing on knowledge
management in clusters. While the use of qualitative and quantitative analyses has been
adopted to improve the reliability of the results, several limitations remain. First, the study
only focuses on two case studies, so the generalisability of results is contextual to the type of
industries considered (low-tech, traditional industries) and the geographical and institutional
context (developed countries in the EU). Furthermore, although the choice of clusters with
similar characteristics might mitigate this problem, other elements than those considered
might have an important impact on the difference in their sourcing strategies, both regarding
the context in which the cluster is embedded (such as the innovative milieu), the features of
the firms (i.e. ownership structures), or of the key markets addressed (e.g., luxury vs. mass
production markets). Further research could replicate a similar analysis on clusters at the same
21
cluster life cycle stage but being diverse along these features. While suggesting that
knowledge strategies vary across cluster life cycle stages, this research focuses just on the
growing and mature stages, leaving open to verify to what extent these results hold also for
the other stages.
The results of this research might be relevant for policy makers interested in supporting the
development of local districts, in order to allow the development of tailored policies (Fornahl
& Hassink, 2017; Belussi & Sedita, 2009; Menzel & Fornahl, 2009). Indeed, the possibility
for clusters to develop and prosper depends on constant knowledge sourcing and exploitation
(Gancarczyk, 2015), knowledge sourcing allows also to prevent cluster decline or lock-in
(Hassink, 2015; Mossig & Schieber, 2016). Knowing what knowledge sources are the most
effective according to the specific life cycle stage will prevent investing in the development of
knowledge infrastructures that are not fit with the real needs in the cluster.
Acknowledgements
We would like to thank two anonymous reviewers for the comments and suggestions that
helped to improve the earlier version of the paper.
The research was financed by the National Science Centre in Poland under the grant
agreement no. 2015/17/N/HS4/00205.
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