Does open innovation work better in regional clusters?
Huang, Fang ; Rice, John
1. INTRODUCTION
The paradigm of open innovation has received considerable academic
and practitioner attention since it was first popularized by Chesbrough
(2003a, 2003b) as a counterpoint to the traditional 'closed
innovation' approach. Although use of the term 'open
innovation' itself is relatively recent, this does not signify the
emergence of altogether new organizational phenomena (Christensen et
al., 2005). Its principles and fundamental ideas build on a strong body
of antecedent knowledge developed in the innovation management
literature, including theories related to research and development
(R&D) externalization, outsourcing, inter-firm collaboration, and
organization-environmental interaction (Carr, 1995; Christensen et al.,
2005; Freeman, 1991; Gronlund et al., 2010). Trott and Hartmann (2009)
have suggested that the 'open innovation' embodies a
repackaging and re-representation of old thoughts on R&D and
innovation management in new theoretical bottles.
Among these previously existing factors that have explored the
interconnectedness of innovating firms, the impact of regionality and
proximity has also been discussed as an element of open innovation
(Cooke, 2005a; Simard and West, 2006; Vanhaverbeke, 2006). However,
little empirical research has been undertaken explicitly exploring the
impact of proximity (to partner firms, and other agencies) on open
innovation effectiveness (Vanhaverbeke, 2006). Thus, in the following
section, we will expound the definitions and characteristics
underpinning these two concepts, the linkage between them, and then
identify the gap in existing literature regarding this important issue.
2. LITERATURE REVIEW
The Definition of Open Innovation
The open innovation model builds upon the notion that innovations
are often not always inspired and developed entirely within a single
firm. It entails "the use of purposive inflows and outflows of
knowledge to accelerate internal innovation, and expand the markets for
external use of innovation, respectively" (Chesbrough, 2006b, p.
1). In essence, open innovation theories suggest that the generation of
innovative outputs is facilitated by more openness towards external
sources of knowledge. This openness encourages the fluidity of knowledge
and information flows between firms.
The emergence of the open innovation approach has been heavily
influenced by changes in our thinking about the fundamental importance
of firms' internal and external knowledge environments. Greater
mobility of skilled workers and more ready transmission of knowledge by
information technology increased the prevalence of inward and outward
'spillovers' between firms and their external environments
(Chesbrough, 2003b). Implicit in this increased focus on knowledge flows
was an acknowledgement of other core characteristics, including the
permeability of firms' transactional and knowledge boundaries
(Gassmann and Enkel, 2004; Pisano, 1990); the emphasis on strong and
effective interactions between firms and their knowledge environment
(Laursen and Salter, 2006; Lichtenthaler, 2008); and the adoption of
open search strategies spanning a wide range of external actors and
players, including customers, suppliers, competitors and research
institutions (Christensen, 2006; Knudsen, 2007; von Hippel, 1988; West
and Gallagher, 2006).
In essence, the open innovation model has been associated with two
major advantages over the closed innovation model. First, it has been
shown to facilitate the transmission of complementary and hence
synergistic, knowledge, expertise and resources across organizational
boundaries (Arora and Gambardella, 1990; Chesbrough, 2005). Second, the
successful integration of externally sourced knowledge with in-house
resources can create complex, differentiated and often inimitable
capabilities (Cassiman and Veugelers, 2006; Lichtenthaler, 2008) that
could sustain competitive advantage over time.
The Definition of Regional Clusters
Derived from the phenomena of industrial agglomerations (Marshall,
1920), Italian industrial districts (Bagnasco, 1977) and studies on the
impact of sectoral firm clustering in specific geographic zones
(Callegati and Grandi, 2005), the definition of regional clusters is
diverse. Porter's (1998b) definition is often used as the starting
point to investigate the concept of clusters (Bergman and Feser, 1999).
According to Porter (1998b, p. 199), a cluster is "a geographically
proximate group of interconnected companies and associated institutions
in a particular field, linked by commonalities and
complementarities". Other researchers have also proposed general
definitions such as "a process of firms and other actors
co-locating within a concentrated geographical area, cooperating around
a certain functional niche, and establishing close linkages and working
alliances to improve their collective competitiveness" (Andersson
et al., 2004, p. 7). Others have suggested clusters are "a
concentration of competing, collaborating and interdependent companies
and institutions which are connected by a system of market and nonmarket
links" (DTI, 1998, p. 22).
Within the variety of these definitions, some common
characteristics of clusters are evident. Summarized by Andersson et al.
(2004), clusters are generally seen to involve geographical
concentration (operationalized by the geographic proximity of
firms' location; engagement between multiple actors--namely between
firms, but also between firms and clients, suppliers, public
authorities, universities and other institutions) (Breschi and Malerba,
2005; Leitao, 2007; Pickernell et al., 2007); and the presence of both
competition and co-operation between these interlinked actors (Andersson
et al. 2004; Ramirez-Pasillas, 2010).
The advantages to emerge from co-location in geographical clusters
that have been observed by previous studies include the creation of
opportunities for innovation and entrepreneurial activities (Eraydin and
Armatli-Korog lu, 2005; Porter, 1998a; Snowdown and Stonehouse, 2006),
the acceleration of innovation diffusion (Breschi and Lissoni, 2001a),
the promotion of local business competitiveness (Van Geenhuizen, 2008),
the advancement of local economic growth, and the enhancement of
regional prosperity (Brown, 2000; Porter, 1998a, 2005), and the
potential to influence, or indeed capture, regional industrial policy
settings (Martin and Rice, 2010). These advantages are realized through
a variety of the mechanisms within localized networks that are spatially
concentrated and enhanced within clusters (Breschi and Malerba, 2005;
Leitao, 2007). It has been found that knowledge can more readily spill
over to close entities, in particular the spillover of tacit knowledge
which needs to be transmitted through interpersonal contact or
inter-firm mobility of skilled workers (Breschi and Lissoni, 2001a;
Breschi and Malerba, 2005).
Open Innovation and Regional Clusters
Evident in the previous review of open innovation and regional
clusters are the variety of complementary notions and thematic overlaps.
These include the presence of inter-organizational network effects,
knowledge flows and spillovers, collaboration within groups of firms and
between firms and other institutions. As Cooke (2005a) has pointed out,
open innovation may partially explain the competitiveness of regional
innovation systems, and Vanhaverbeke (2006) has also noted that firms
embedded regional clusters are more inclined to employ open innovation
strategies than others.
Given these observations of co-occurrence, a further investigation
of the linkage between these two concepts is timely. Vanhaverbeke (2006)
has suggested that the link between open innovation and regional
development is a promising area of research. Simard and West (2006) also
recognized regional clusters as an ideal setting for the analysis of
open innovation. However, other than the work of Cooke (1998, 2005a,
2005b), who explicitly studied the relationship between open innovation,
clusters and regional innovation systems, there has been limited
research around this issue so far.
In order to address this gap in the literature, we attempt here to
establish a conceptual framework based on the intersection of these two
theoretical streams. As discussed above, two key elements commonly
observed in both concepts, namely the networking with multiple actors
and agents, and the presence of knowledge spillovers and flows, will be
the two areas of research focus for our study.
Another core issue underpinning the open innovation philosophy is
the potential synergy by integrating internal and external innovations.
Researchers have suggested that although the open innovation model
underlines more external research efforts, in-house R&D need not be
thus seen as obsolete (Chesbrough and Crowther, 2006; Lichtenthaler,
2008). This potentially synergistic relationship is of great importance
in the context of regional clusters where both internal research and
external sources of innovation are active, as is often the case in
clusters focusing on high technology production and manufacturing.
Based on these considerations, these three main aspects--networking
with multiple sources, knowledge spillovers and flows, and relationship
between internal R&D and external research will be considered as the
areas of focus for our research. An important question, which hitherto
remains unanswered, is how geography affects open innovation practices
of firms (West et al., 2006). We focus on the question regarding whether
positive open innovation outcomes will be more evident in regional
clusters.
Our study, based on empirical evidence from a sample of clustered
and non-clustered firms, will assess these issues. Grounded on the
theoretical framework which focuses on the three central dimensions to
be explored, we will propose our hypotheses on the basis of both open
innovation and regional clusters literature in the following section.
3. HYPOTHESES
Networking with External Sources
Clusters have typically been understood as networks of
interconnected companies and institutions (Breschi and Lissoni, 2001b;
Porter, 1998a). The general benefits of networking have been widely
observed by previous literature. These include the mitigation of
resource and capability absences (Ahuja, 2000; Powell et al., 1996;
Vanhaverbeke, 2006); the sharing of complementary skills and resources
(Hagedoorn and Duysters, 2002); the facilitation of knowledge and
innovation diffusion (Cowan, 2005); and the enhancement of the market
power of participant firms, especially in nascent technologies (Human
and Provan, 1996).
Open innovation theories underline the importance of networking
that draws upon a wide range of external knowledge sources, including
focal firms, universities, research labs, venture capitalists, and other
knowledge generating agencies (Simard and West, 2006). It has been
widely recognized that the diverse knowledge bases outside the
firm's boundary act as a driver of a firm's internal growth,
value creation and innovation performance (Gronlund et al., 2010;
Laursen and Salter, 2006).
Open innovation strategy entails a diverse set of linkages leading
to two basic types of networking. First are the inter-firm
collaborations between focal firms and their suppliers, their customers
and potentially their competitors (von Hippel, 1988; Vanhaverbeke, 2006;
Vanhaverbeke and Cloodt, 2006). Inter-organizational networks and
collaborations play a significant role in advancing the capacity of
firms to promote innovation (Faems et al., 2005; Martin and Rice, 2012;
Nieto and Santamaria, 2007; Porter and Ketels, 2003). Such network
arrangements can assist firms in capturing complementary knowledge and
capabilities, enhancing potential variety and availability of external
knowledge, and creating values through the whole value chain from the
early stages of technology development towards the commercialization of
innovation outputs (Chesbrough and Rosenbloom, 2002). Inter-firm
networks also signify the membership in a local community of knowledge
which will increase the interdependence and mutual innovation benefits
between member firms (Simard and West, 2006).
This contribution to innovativeness and performance of networked
firms has been also widely supported by empirical studies (e.g. Deeds
and Hill, 1996; Faems et al., 2005; Hagedoorn and Schakenraad, 1994).
Given these advantages, it is believed that inter-firm networking namely
the linkages between firms will generally have a positive effect on
innovation performance of firms regardless of their localization. On the
basis of that, we hypothesize:
H1a:--Inter-firm networking will have a significant effect on
innovation performance of both clustered firms and non-clustered firms.
Universities and research institutes are also recognized as an
important and primary source of knowledge that facilitate open
innovation outcomes (Creplet et al., 2001; Simard and West, 2006). The
close cooperation with these knowledge-based institutions can help firms
to keep up with the latest technological breakthroughs and explore the
application and commercial potential of these technologies
(Vanhaverbeke, 2006). However, compared with inter-firm networking, the
practicalities of university (research institute)-firm engagement as a
source of innovation activities present some significant challenges.
First, universities and research institutes often focus on
theoretical or fundamental research domains where the created knowledge
may not be directly applicable to industries or specific innovation
process of firms (Quintas et al., 1992; Simard and West, 2006).
Moreover, they are usually linked with firms by the contractual
arrangements (Breschi et al., 2005), which entails the accrual of search
and transaction costs (Christensen et al., 2005). The cultural
dissimilarities between firms and universities also create indirect
costs involving extra search and negotiation efforts, and often
resulting in constrained knowledge flows (Gallini, 2002; Katila and
Ahuja, 2002).
There is some evidence that suggests that regional proximity
between firms and universities can be an important driver of
knowledge-based collaboration between these organizations (Chesbrough,
2003b; Fabrizio, 2006; West et al., 2006). Regionally co-located firms
may have face-to-face contacts with university researchers, facilitating
specialized research which accords with the firm's demand (Breschi
and Lissoni, 2001a) and helping to mediate some of the cultural barriers
to knowledge exchange discussed above (Jaffe et al., 1993). Furthermore,
it also has been found that physical proximity tends to lower the direct
and indirect transactional, search and knowledge transmission costs
between network participants (Breschi and Malerba, 2005).
In summary, firms co-located near universities might tend to enjoy
greater benefits from firm-university (firm-research institute) linkages
than will non-clustered firms.
This discussion can be stated in the following hypothesis:
H1b:--The effect of firm-university (firm-research institute)
networking on innovation performance of clustered firms will be greater
than that of non-clustered firms.
Knowledge Flows and Spillovers
Knowledge spillover is an intentional, or unintentional, process
whereby knowledge transfers between organizations. With the closed
innovation model, knowledge spillovers were usually viewed as the
unwanted and unintended byproduct of innovation processes. As a cost of
doing R&D, spillovers may reduce the profits available from
investment in innovation (Chesbrough, 2006b; von Hippel and von Krogh,
2003).
In contrast to this traditional model where spillovers were seen as
a negative externality of knowledge creation and innovation, firms
operating with an open innovation strategy purposively facilitate
spillovers and enable the disclosure of knowledge and technology in
order to participate in collaborative network arrangements (Schmidt,
2006). These spillovers become valuable opportunities for developing new
business models and exploiting innovation commercialization channels
(Chesbrough, 2006a). The openness of innovation enhances the fluidity of
knowledge flows and catalysts the knowledge and information exchanges
between firms. Spillovers can also help overcome the intra-firm
knowledge asymmetries while diversifying the firm's knowledge bases
(Chesbrough, 2006b; Cooke, 2005b).
Given the importance of knowledge flows and spillovers to open
innovators, we hypothesize that:
H2a:--Knowledge flows and spillovers will have a significant effect
on innovation performance of both clustered firms and non-clustered
firms.
Proximity-driven knowledge flows are defined as localized knowledge
spillovers (LKS) in regional clusters (Cooke, 2004; Zucker et al.,
1998). The effects of LKS and general knowledge spillovers on open
innovation performance might differ due to the heterogeneity of
knowledge stocks, as well as the variety in the way in which knowledge
flows between organizations (Audretsch and Feldman, 2004).
Audretsch's (1998) study indicated that there is a higher
propensity for innovation within spatial clusters, with greater tacit
knowledge that needs to diffuse through direct and repeated contacts.
This suggests that the flows of knowledge between co-located entities
discussed by some studies (e.g. Jaffe et al., 1993) are driven by
various forms of inter-firm contacts and ready access to a pool of
shareable tacit knowledge (Audretsch and Feldman, 2004).
This finding is consistent with Breschi and Malerba (2005) who
pointed out the specific properties of tacit knowledge, namely its
dependence on co-located agents to transit as opposed to the codified
knowledge that can transfer without geographical constraints. Breschi
and Malerba (2005) also note that social links and close contacts
required by tacit knowledge flows would be fundamentally important to
encourage individual firms to tap into the localized knowledge bases and
engage in collective learning processes essential for their innovation.
In that sense, we hypothesize that the tacit knowledge will play a
more important role in facilitating innovation among clustered firms
than non-clustered firms.
H2b:--The effect of the spillovers and flows of tacit knowledge on
innovation performance of clustered firms will be greater than that of
non-clustered firms.
The Relationship between Internal R&D & External Research
From the above discussion it might be assumed that under the open
innovation paradigm, firms might forego the role of internal R&D,
while compensating for it by drawing on knowledge and expertise from a
broad range of external sources (Laursen and Salter, 2006). This
contention tends to ignore potential synergy-based complementarities
which may be generated through a simultaneous combination of both
'in-house' research and the sourcing of external knowledge and
technologies (Ettlie and Reza, 1992). Thus in-house R&D need not
become obsolete when open strategies are followed--indeed openness may
even stimulate internal research investments in search of such synergies
(Howells, 1999; Veugelers, 1997). Further, in addition to the
traditional role of generating innovation alone, in-house R&D may
act as a catalyst to the transformative effectiveness once the external
knowledge reaches the focal firm (Cohen and Levinthal, 1989; Lane et
al., 2006). The overall status of knowledge base within the firm could
be improved by such way of integrative knowledge management (Cassiman
and Veugelers, 2006; Lichtenthaler and Lichtenthaler, 2009).
This complementarity between internal R&D and open innovation
practices has also been illustrated in empirical studies on open
innovation (e.g. Chesbrough and Crowther, 2006; Lichtenthaler, 2008).
Based on these considerations, we hypothesize that internal R&D can
generally benefit innovation performance in the contexts of open
innovation for both clustered firms and non-clustered firms.
H3a:--Internal R&D will positively affect innovation
performance as well as enhancing the role of external research, for both
clustered firms and non-clustered firms.
Expanding on Hypothesis 3a, we would anticipate that the relative
impacts of internal and external research might differ between clustered
and non-clustered firms. The density of network ties among multiple
actors and the fluidity of knowledge flows may create variance in the
impacts of internal research between the two groups. According to Leitao
(2007), firms in clusters may access significant research discoveries
without carrying out much internal research of their own. This might be
especially the case for start-ups who might survive by relying on
external institutional and organizational networks while not deploying
their scarce financial and operational resources as extensively to
in-house R&D (Simard and West, 2006). Simard and West (2006) also
noted that networks that are facilitated by geographic proximity could
play a crucial role for member firms in building ties to the
complementary knowledge while establishing commercialization pathways.
Thus internal R&D may have a comparatively lower impact on
cluster-based firms than those that are not embedded in regional
clusters. This discussion can be stated in the following hypothesis:
H3b:--Internal R&D will have a greater effect on innovation
performance of non-clustered firms than that of clustered firms.
4. METHODS
Data
The data source for this study is from the Flash Eurobarometer 187
"Innobarometer among enterprises in the EU and other European
countries" telephone survey. This survey was conducted in 2006 by
the Gallup Organization on behalf of the DG Enterprise and Industry of
the European Commission (The Gallup Organization, 2006).
This particular Flash Eurobarometer survey was designed to provide
detailed information on the clustering-related issues among companies in
the various European countries, and their managers' views on the
opportunities and challenges of companies operating in clusters (The
Gallup Organization, 2006). The target group for the survey was
companies with 20 or more employees operating in the 25 Member States of
the European Union, the accession countries Bulgaria and Romania, and
the candidate countries Turkey and Croatia, as well as Switzerland,
Norway and Iceland. Thus firms from 32 European countries were included
in total (The Gallup Organization, 2006). The desired sample size was
100 in an average-sized country, although this number varied based on
the size of the country. In Germany, Spain, France, Italy, Poland and
the UK, it was around 200; while in Iceland and Malta, it was around 40;
and for other smaller countries (i.e. Estonia, Cyprus, Latvia,
Lithuania, and Luxembourg) and non-EU countries (i.e. Bulgaria, Croatia,
Romania, Turkey, Switzerland and Norway), the target sample size was
around 66 (The Gallup Organization, 2006).
The person surveyed in the target group was top-level executive(s)
of each company such as the General Manager, Financial Director or
significant owner. The original questionnaire in English was translated
to the local national languages in different countries by rigorous
back-translation and central verification procedures to ensure the
validity of the localized questionnaires (The Gallup Organization,
2006).
This secondary database was valuable for this empirical research in
two respects. First, Europe has a rich tradition of spatial clustering
and industrial districts (Audretsch and Feldman, 2004; Breschi and
Lissoni, 2001a, 2001b). The role of geographic networking in promoting
entrepreneurship and overcoming size disadvantage for small and medium
enterprises (SMEs) in Europe has been emphasized by many studies (e.g.
Pyke et al., 1990). Europe thus provides a valuable context to study
firms operating in regional clusters and to conduct a comparison with
firms which are not co-located. Furthermore, the dataset provided
firm-level unit record data of European firms, suitable for an analysis
of the variance of organizational practices as proposed by our
hypotheses, and an analysis of differentiated practices between the
cluster/non-cluster subgroups.
Subsamples
In order to test our hypotheses, we divided the sample drawn from
the survey of Flash Eurobarometer 187 into two sub-samples, namely firms
belonging to clusters and firms not belonging to clusters. This division
was on the basis of the survey question "do you consider that your
firm is part of a cluster, or not?"
The following definition of clusters was clearly given to
respondents: "Clusters are geographically close groups of
interconnected companies, suppliers, service providers, and associated
institutions in a particular field. In cluster all these actors are
linked in several ways ... Clusters are often working in a particular
region, and sometimes in a single town" (The Gallup Organization,
2006, p. 2). This definition is consistent with the definition and scope
of regional clusters employed in our study.
According to the binary responses (Yes or No) to this question, we
divided the whole sample into subsamples consisting of 2,297 clustered
firms and 1,171 non-clustered firms. The basic attributes of
observations in these two subsamples such as age, size and country
distributions are presented in the appendices.
5. MEASURES
Dependent Variable
The dependent variable (Innovation) in this study is the
dichotomous response to the question of whether a company had introduced
new or significantly improved products or services in the last two
years, namely during the period 2004-2006 for respondents in this study.
The original responses (value 1 for yes and value 2 for no) were recoded
into more semantically appropriate dichotomous variables with a value of
zero (0) if no such innovation had occurred, and one (1) if it had.
Given the binary feature of the dependent variable (coded 0 or 1), the
binary logistic regression model was employed in the analysis.
Independent Variables
Data was gathered on inter-firm networking (Interfirm). The survey
provided differentiated evidence regarding the size of firms that the
focal firm cooperated with. Firms were asked whether they had cooperated
with large firms (Interfirm1) or small and medium enterprises
(Interfirm2) in the cluster (or in the wider region, for non-clustered
firms). From this survey question, two variables regarding the
networking with large firms and SMEs were derived. Both of these are
dummy variables, taking the value of one (1) when the firm indicated
that it had used such form of inter-firm networking and zero (0)
otherwise.
Data was also gathered on the linkages with universities (Uni) and
research institutes (RI). Firms were also asked whether they cooperated
with "universities and other education institutions"; or
"public laboratories or research centers". The answers to
these two questions form the constructs of variables in regard to the
linkages with universities (Uni) and research institutes (RI). These
were provided as binary responses, which take the value of 1 for the
response yes and 0 for no, after recoding of the original responses.
The construct of knowledge flows and spillovers involved in
Hypothesis 2a and 2b is measured based on three survey questions. Firms
were asked whether they exchanged information on technology (Explicit1);
whether they exchanged information on market characteristics
(Explicit2); and whether they exchanged information and knowledge on
best practices (Tacit). The original responses were recoded into dummy
variables with a value of zero (0) if no such form of knowledge exchange
had occurred, and one (1) if it had. We interpreted the first two forms
of knowledge exchange as being focused on explicit knowledge, and the
third as being a measure of tacit knowledge, although we acknowledge the
limitation of this typology.
Firms were also asked to report on the role of internal R&D
(Internal) and external research (External). Internal R&D was
measured by the question relating to whether the firm carried out
research in its own laboratories. Firms were also asked whether they
contracted out research to other firms, universities or research
institutes. These are included in our model as dummy variables taking
the value one (1) for yes and zero (0) for no.
Control Variables
Basic organizational attributes which have been utilized as control
variables in this study are firm size and firm age. Firm size (Size) is
expressed as a categorical variable with ordinal values of the number of
employees--0 (less than 20, which had been excluded from the original
microdata by the survey conductors), 1 (20-49), 2 (50-249), 3 (250-499),
4 (500 or more). Firm age (Age) is also a categorical variable based on
an ordinal scale of measurement, taking the value from 3 (before 1986),
2 (between 1986 and 2001) to 1 (after 2001).
Another two control variables included in this are industry dummies
(Industry), and a measure of density of the given industry (Density).
While widespread across industries, the open innovation phenomenon is
influenced by industry-specific characteristics (Audretsch and Feldman,
2004; Chesbrough and Crowther, 2006; Laursen and Salter, 2006). In order
to control for the different effects of industry heterogeneity on open
innovation practices and clustering activities, our study include 14
dummy variables for industry categories. The original single variable
with aggregated responses of industry categories was transformed to
fine-grained measures of industry dummies, namely (1) ICT and
Communication equipment; (2) Aeronautics and Space; (3) Pharmaceuticals
& medical devices; (4) Construction (materials, equipment, heavy
construction); (5) Automotive; (6) Metal manufacturing; (7) Plastics;
(8) Chemical products; (9) Textiles, leather, footwear; (10) Energy;
(11) Production equipment (machinery, electrical); (12) Food; (13)
Entertainment; and (14) Services.
Associated with the industry dummy, the effect of the density of
this industry (Density) was also included. This was measured by the
question of whether the concentration of firms working in the same
business sector as the focal firm's was higher, similar, or lower
than elsewhere in the country. The original responses were recoded from
1 to 3 to ensure that the larger values represented a higher density of
the given industry.
6. RESULTS
Descriptive statistics and correlations for both subsamples are
displayed in Table 1 and Table 2. Table 3 shows our results of binary
logistic regressions on the two subsamples. These lead to our findings
with regard to previously-stated hypotheses. For the subsample of
clustered firms, the values of Cox & Snell R Square (16.3%) and
Nagelkerke R Square (23.0%) indicate a reasonable goodness of fit for
the model. This is also the case for the non-clustered firm subsample
where the Cox & Snell R Square is 18.9% and Nagelkerke R Square is
25.3%. The significant Chi-square (p < .001 for both) for both models
also provides evidence of their overall significance.
With regards to Hypothesis 1a, which suggests that inter-firm
networking will improve firms' innovation performance, we find that
only the variable Interfirm2 (i.e. networking with smaller firms)
positively and significantly (p < .05 for both) affects with the
dependent variable of both subsamples. Thus H1a is not fully supported.
We suggest that this may be an artefact of the limited number of large
companies available for collaboration for many of the responder firms
(evidence of which is provided in the descriptive statistics of firm
size in appendices). Regarding H1b, that hypothesizes that the use of
firm-university (firm-research institute) linkages will have greater
impact on innovation performance of clustered firms than that of their
non-clustered counterparts, the variable Uni is positive and significant
(p < .05) in the model of clustered firms, while insignificant (p
> .10) in the non-clustered subsample. We note, however, that the
coefficients of research institutes (variable RI) are not significant (p
> .10) for observations from both subsample groups. Therefore, H1b is
partially supported.
H2a suggests that the flows and exchanges of knowledge will
positively affect the innovation performance of firms in both
subsamples. The variables Explicitl, Explicit2 and Tacit are all
significant and positive in anticipating the innovation performance of
clustered firms (p < .01, p < .001 and p < .001 respectively)
while only Explicit2 (namely the knowledge on market) is significant for
non-clustered firms (p < .001). H2a is thus partially supported,
while H2b is fully supported, as tacit knowledge (variable Tacit) is
only significant and positive (p < .001) for the subsample of
clustered firms. This suggests that spillovers of tacit knowledge will
have greater impact on innovation for clustered firms in comparison to
non-clustered firms.
The coefficients for the variable measuring internal R&D
(variable Internal) are found to be positive with strong significance
(both p < .001) for both of the subsample groups. This supports our
assertion in H3a that even in the context of openness, internal R&D
is still a positive antecedent to innovation performance for both
clustered firms and non-clustered firms. Moreover, we note that the
magnitudes of the use of external research between both subsamples are
similar, while the coefficient of internal R&D for non-clustered
firms is larger than for clustered firms. As such, H3b predicting that
clustered firms might have a lower reliance on internal R&D for
innovation, finds support from our data.
Of our control variables, the firm size (variable Size) seems to
only affect the innovation performance of clustered firms, and Firm age
(variable Age) does not present significantly for either of the
subsamples. It is shown that for clustered-firms, their belonging to the
Textiles, Leather, Footwear; and Production Equipment (Machinery,
Electrical) sectors have highly significant effect (p < .01 and p
< .05 respectively) on innovation performance. Services sector status
has a relatively weak positive impact (p < .10) on innovativeness.
For non-clustered firms, there is a significant positive effect (p <
.05) for Metal Manufacturing, and a weak significant effect for
Production Equipment (Machinery, Electrical) and Food sectors.
We interpret these results as an indicator that, for most European
firms, the effects of geography are still crucial to the innovative
capacity of the manufacturing industries. However, the relationship
between high-technology industry status and innovation performance is
not evident for observations in both our subsamples. Moreover, whatever
industry the firm operates in, the density of that given industry
(variable Density) is likely to positively affect the innovation
performance (p < .10 for both subsamples).
7. DISCUSSION
This study attempts to empirically investigate an under-explored
area in the open innovation literature, namely the relationship between
open innovation and geographical clustering. We have explored whether
open innovation is more pervasive and effective in firms within regional
clusters.
We examine this question from three crucial themes underpinning
open innovation philosophies, namely firms accessing external sources
within network, the presence of knowledge flows and spillovers, and the
relationship between internal R&D and external research.
According to the results of our study, generally speaking close
geographical proximity within regional clusters provides positive, and
significant, enhancements to open innovation practices in terms of firm
innovativeness. This finding is consistent with many theoretical
propositions in the open innovation literature (e.g. Simard and West,
2006; West et al., 2006).
Specifically, we find evidence that cluster-based (vis-a-vis
non-cluster based) firms are found to have more beneficial
firm-university linkages, more efficient knowledge flows and tacit
knowledge exchanges, and are comparatively less reliant on internal
research. This suggests that the advantages arising from the open
innovation strategy can be enhanced in the context of regional clusters.
This finding is also expected to provide insights into the effectiveness
of open innovation in different regions or countries, and in turn the
connection of open innovation to local innovation systems, regional
economics and national competitiveness (Vanhaverbeke, 2006).
Many of the enhancements regarding open innovation's impacts
can be attributed to some of the supportive general features of regional
clusters. Clusters are characterized by active knowledge flows among a
diverse set of sources from organizations and institutions (Cooke,
2005b; Simard and West, 2006). These key sources are of great importance
in terms of their supporting of either formal information exchange or
tacit knowledge flows across multiple entities to facilitate innovation
(Bierly and Daly, 2007). The localized knowledge spillovers are
considered to have more advantages than other spillovers because
knowledge always tends to transmit more efficiently among closely
located actors (Breschi and Malerba, 2005; Jaffe et al., 1993), whether
through inter-organizational contact or through individual mobility
(Almeida and Kogut 1999). Furthermore, geographical proximity can
stimulate the absorption of knowledge spillovers and establish a thick
network of knowledge sharing through effective communication means
within clusters (Breschi and Malerba, 2005; Cooke, 2004). Other than
these explanations, our study also tries to shed light on some
underlying determinants and mechanisms to explain why regional clusters
are favourable for the application of open innovation.
The economic profits of open innovation can be optimized by the
direct sales of technology (outbound licensing) and indirect returns
through open standards, relatively fluid information flows and free
knowledge exchange with limited transaction costs and potentially fewer
royalty requirements (West, 2006; West and Gallagher, 2006).
Despite its advantages over the closed innovation model, open
innovation is not problem-free (Elmquist et al., 2009). Its potential
drawbacks have been addressed by recent research. First, some open
innovation approaches might be associated with high coordination costs
resulting from involving external parties in the innovation process, and
transaction costs arising from contractual negotiations and information
access (Christensen et al., 2005). These costs also include indirect
costs and risks if the knowledge inflows (to firms) are less valuable
than the outflows to the firms' competitors (Simard and West,
2006). In this sense, the difficulty in finding the right balance
between disclosing certain knowledge to benefit from openness and
protecting core knowledge to maintain an organisation's
competitiveness exposes firms to the 'paradox of openness'
(Laursen and Salter, 2005; West and Gallagher, 2006). As a result, firms
are more likely to benefit from openness only when these potential
returns from knowledge spillovers can outweigh the risks and costs
related to open practices (Schmidt, 2006).
Regional clusters are likely to offset the downsides of open
innovation and overcome the potential disadvantages of this new
innovation mode in a variety of ways. Primarily, we show that the costs
associated with open strategies could be reduced in clusters. Spatial
proximity lowers the direct costs or relational collaboration, including
search and negotiation with partners, assessing information and
knowledge bases, as well as the costs of knowledge transmission,
particularly for firm-university ties (Audretsch and Feldman, 2004;
Breschi and Malerba, 2005; Simard and West, 2006).
As a result of these reduced costs, firms could gain more benefits
from the linkages with universities and research centres. The
localization also tends to reduce the indirect costs originating from
the uncertainty in the relationship with collaborated firms, and tends
to mitigate conflicts between inbound and outbound knowledge flows (Rice
and Juniper, 2003). As the tacit knowledge flows among firms mainly take
the means of informal contact between, and inter-firm mobility of,
skilled workers (Breschi et al., 2005), there is probability that
knowledge might flow to, and be utilized by, potential competitors.
Firms thus may choose to stop knowledge disclosure and sharing in order
to avoid unintended knowledge spillovers. This means the transfer of
tacit knowledge requires a high degree of mutual trust and
interdependence between partners.
Clusters can alleviate this uncertainty and unwillingness through
the involvement of close social networks based on reciprocal trust and a
cooperative relationship (Breschi and Lissoni, 2001a; Breschi and
Malerba, 2005). Firms, and people, in regional clusters are more likely
to establish trust based on their past interactions with others (Simard
and West, 2006). This could encourage more frequent and repeated
interactions between firms, and strengthen the formal or informal ties
among them (Breschi and Lissoni, 2001b). Trust acts as a coordinating
mechanism among networked firms in clusters (Powell, 1990). Reciprocal
trust and reduced costs can also be used to explain greater reliance on
external research providers such as partner firms, universities or
research institutes than their in house R&D for clustered firms.
Clearly, there is some endogeneity in this analysis. By definition,
a combination of competition and co-operation is one of the core
characteristics of clusters (Breschi, and Malerba, 2005; Callegati and
Grandi, 2005). It is suggested that facing vigorous local competition,
cooperation among interconnected entities can greatly benefit knowledge
sharing in clusters and boost the regional productivity (Brown, 2000;
Leitao, 2007). This is also in essence akin to the philosophy involved
in open innovation theories. Open innovation emphasizes the significant
synergy effects of shared knowledge creation to exchange various forms
of codified or tacit knowledge (Lavie and Rosenkopf, 2006; Chesbrough,
2003b). With the core of this process termed as "connect and
develop", open innovation is especially concerned with the benefits
brought by cooperative relationship between firms in their competition
for innovation (Sakkab, 2002).
8. CONCLUSION
Most benefits proposed by the advocates of open innovation are
based on the ideas of interdependence, trust, and mutual reciprocity
which greatly facilitate knowledge sharing, transfer and benefits
appropriation. The findings of our research illustrate the importance of
cluster-based context within which these underpinning benefits of open
innovation are expected to be optimized.
Regional clusters are believed to provide an environment within
which the direct costs associated with open strategies (such as
contractual, knowledge search costs and indirect costs particularly in
terms of knowledge transmission costs), the uncertainty in collaborative
relationships and the conflicts between inbound and outbound knowledge
flows, could be minimised.
This research provides positive evidence regarding the
circumstances by which open innovation's benefits could be
enhanced. We find that the effectiveness of open innovation could be
greatly enhanced when the advantages of openness outweigh its costs and
potential risks. In such circumstances, firms will optimize the benefits
from adopting open strategies. Our findings show that regional clusters
present a highly supportive setting where unrestricted knowledge
transfers can occur, sustained by a higher degree of expected
reciprocity and limited transactional and other costs.
This facilitated knowledge transfer is realized by the more
efficient firm-university linkages and freer flow of tacit knowledge
between cluster-based firms. However, the main limitation of this
research lies in the simplification of the sources of explicit and tacit
knowledge, which could be addressed in future research when data allows.
This research not only makes valuable theoretical contributions to
both the regional studies and open innovation literatures, based on
empirical evidence from a large sample, but also provides practical
implications for policy makers and organizations. It is strongly
suggested that the open innovation mode could be effectively implemented
and actively encouraged within regional clusters to drive regional
innovation performance, and also to create a collaborative arrangement
among firms in a competitive local environment. Under such
circumstances, local entrepreneurs in regional clusters also are more
likely to take advantage of external knowledge sources to successfully
innovate their start-ups.
APPENDICES
Sample Attributes of Clustered Firms and Non-Clustered Firms
A. In which year the company was established.
Sample of Clustered Firms
Frequency Valid Percent Cumulative Percent
Before 1986 1217 53.0 53.0
1986-2001 923 40.2 93.2
After 2001 135 5.9 99.0
DK/NA 22 1.0 100.0
Total 2297 100.0
Sample of Non-Clustered Firms
Frequency Valid Percent Cumulative
Percent
Before 1986 606 51.8 51.8
1986-2001 514 43.9 95.6
After 2001 46 3.9 99.6
DK/NA 5 .4 100.0
Total 1171 100.0
Source: the Authors.
B. How many employees in the company.
Sample of Clustered Firms
Frequency Valid Percent Cumulative Percent
20-49 875 38.1 38.1
50-249 859 37.4 75.5
250-499 277 12.1 87.5
500 or more 286 12.5 100.0
Total 2297 100.0
Sample of Non-Clustered Firms
Frequency Valid Percent Cumulative Percent
20-49 487 41.6 41.6
50-249 465 39.7 81.3
250-499 104 8.9 90.2
500 or more 115 9.8 100.0
Total 1171 100.0
Source: the Authors.
C. Country distribution of observations in sample.
Sample of Clustered Firms
Frequency Valid Percent Cumulative Percent
Belgium 82 3.6 3.6
Czech Rep. 23 1.0 4.6
Denmark 52 2.3 6.8
Germany 105 4.6 11.4
Estonia 45 2.0 13.4
Greece 50 2.2 15.5
Spain 40 1.7 17.3
France 149 6.5 23.8
Ireland 148 6.4 30.2
Italy 195 8.5 38.7
Cyprus 5 .2 38.9
Latvia 47 2.0 41.0
Lithuania 38 1.7 42.6
Luxembourg 25 1.1 43.7
Hungary 53 2.3 46.0
Malta 31 1.3 47.4
Netherlands 26 1.1 48.5
Austria 51 2.2 50.7
Poland 56 2.4 53.2
Portugal 97 4.2 57.4
Slovenia 44 1.9 59.3
Slovakia 73 3.2 62.5
Finland 90 3.9 66.4
Sweden 84 3.7 70.0
UK 270 11.8 81.8
Bulgaria 82 3.6 85.4
Croatia 68 3.0 88.3
Romania 63 2.7 91.1
Turkey 86 3.7 94.8
Norway 54 2.4 97.2
Switzerland 37 1.6 98.8
Iceland 28 1.2 100.0
Total 2297 100.0
Sample of Non-Clustered Firms
Frequency Valid Percent Cumulative Percent
Belgium 18 1.5 1.5
Czech Rep. 22 1.9 3.4
Denmark 17 1.5 4.9
Germany 45 3.8 8.7
Estonia 16 1.4 10.1
Greece 18 1.5 11.6
Spain 39 3.3 14.9
France 75 6.4 21.9
Ireland 77 6.6 27.9
Italy 164 14 41.9
Cyprus 1 .1 42.0
Latvia 13 1.1 43.1
Lithuania 27 2.3 45.4
Luxembourg 12 1.0 46.5
Hungary 55 4.7 51.2
Malta 10 .9 52.7
Netherlands 8 .7 57.0
Austria 51 4.4 59.1
Poland 24 2.0 63.9
Portugal 56 4.8 67.6
Slovenia 44 3.8 71.6
Slovakia 47 4.0 72.5
Finland 10 .9 72.5
Sweden 20 1.7 74.2
UK 136 11.6 85.8
Bulgaria 12 1.0 86.8
Croatia 57 4.9 91.7
Romania 17 1.5 93.2
Turkey 29 2.5 95.6
Norway 13 1.1 96.8
Switzerland 29 2.5 99.2
Iceland 9 .8 100.0
Total 1171 100.0
Source: the Authors.
ACKNOWLEDGEMENTS:
We thank two anonymous reviewers for their valuable comments on the
earlier draft of this paper.
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Fang Huang
Lecturer, School of Management and Governance, Murdoch University,
Perth, Western Australia, 6150, Australia.
Email: a.huang@murdoch.edu.au
John Rice
Senior Lecturer, Griffith Business School, Griffith University,
Gold Coast, Queensland, 4222, Australia.
Email: j .rice@griffith.edu.au
Table 1. Means, Standard Deviations, and Correlations for the
Sample of Clustered Firms.
Variable Mean S.D. 1 2 3
1. Innovation 0.70 0.46
2. Interfirm1 0.71 0.45 .18 **
3. Interfirm2 0.82 0.38 18 ** .45 **
4. Uni 0.61 0.49 .21 ** .21 ** .20 **
5. RI 0.42 0.49 .19 ** .23 ** .18 **
6. Ecpilict1 0.72 0.45 .21 ** .20 ** .23 **
7. Explicit2 0.77 0.42 .18 ** .20 ** .27 **
8. Tacit 0.74 0.44 .21 ** .23 ** .26 **
9. Internal 0.38 0.48 .27 ** .13 ** .02
10. External 0.37 0.48 .22 ** .12 ** .08 **
11. Size 1.99 1.00 .14 ** .15 ** .05 *
12. Age 2.48 0.61 -.01 -.10 ** -.10 **
13. Density 2.71 0.57 .01 -.02 -.07 **
Variable 4 5 6 7 8
1. Innovation
2. Interfirm1
3. Interfirm2
4. Uni
5. RI .40 **
6. Ecpilict1 .20 ** .22 **
7. Explicit2 .13 ** .10 ** .36 **
8. Tacit .22 ** .17 ** .41 ** .37 **
9. Internal .23 ** .31 ** .12 ** .01 .07 **
10. External .28 ** .30 ** .13 ** .07 ** .16 **
11. Size .19 ** .14 ** .05 ** .02 .07 **
12. Age .07 ** .07 ** -.03 -.03 -.04 *
13. Density -.05 ** .00 -.01 -.10 ** -.04 *
Variable 9 10 11 12
1. Innovation
2. Interfirm1
3. Interfirm2
4. Uni
5. RI
6. Ecpilict1
7. Explicit2
8. Tacit
9. Internal
10. External .30 **
11. Size .22 ** .20 **
12. Age .06 ** .02 .13 **
13. Density .05 ** -.01 -.08 ** -.03
n = 2297; ** Correlation is significant at the 0.01 level
(one-tailed); * Correlation is significant at the 0.05 level
(one-tailed)
Source: the Authors.
Table 2. Means, Standard Deviations, and Correlations for the Sample
of Non-Clustered Firms.
Variable Mean S.D. 1 2 3 4
1. Innovation 0.61 0.49
2. Interfirml 0.62 0.49 .17 **
3. Interfirm2 0.77 0.42 15 ** .51 **
4. Uni 0.51 0.50 .15 ** .21 ** .20 **
5. RI 0.31 0.46 .15 ** .14 ** .11 ** .32 **
6. Ecpilictl 0.51 0.50 .15 ** .17 ** .14 ** .13 **
7. Explicit2 0.56 0.50 .19 ** .17 ** .15 ** .12 **
8. Tacit 0.50 0.50 .10 ** .16 ** .11 ** .19 **
9. Internal 0.31 0.46 .32 ** .09 ** .04 .19 **
10. External 0.28 0.45 .21 ** .16 ** .13 ** .27 **
11. Size 1.87 0.94 .11 ** .10 ** .02 .23 **
12. Age 2.48 0.57 .01 -.01 -.06 * .03
13. Density 2.79 0.51 .00 -.06 * -.08 ** -.09 **
Variable 5 6 7 8 9 10
1. Innovation
2. Interfirml
3. Interfirm2
4. Uni
5. RI
6. Ecpilictl .14 **
7. Explicit2 .09 ** .46 **
8. Tacit .14 ** .45 ** .47 **
9. Internal .32 ** .09 ** .05 .08 **
10. External .28 ** .17 ** .11 ** .16 ** .30 **
11. Size .10 ** .-.01 .08 ** .06 * .17 ** .21 **
12. Age .03 -.07 * -.02 -02 .05 * .-00
13. Density -.04 -.04 -.02 -.04 -.03 -.08 **
Variable 11 12
1. Innovation
2. Interfirml
3. Interfirm2
4. Uni
5. RI
6. Ecpilictl
7. Explicit2
8. Tacit
9. Internal
10. External
11. Size
12. Age .17 **
13. Density -.04 .01
n = 1171; ** Correlation is significant at the 0.01 level
(one-tailed); * Correlation is significant at the 0.05 level
(one-tailed)
Source: the Authors.
Table 3. Results of Binary Logistic Regression Analysis for Innovation
Performance.
Dependent Variable [right arrow] Innovation Performance
(Innovation)
Independent Variables & Clustered Non-Clustered
Control Variables
[down arrow]
INDEPENDENT VARIABLES
Inter-firm Networking
With Large Firms (Interfrml) 0.184 0.319 (+)
With SMEs (Interfirm2) 0.367 * 0.394 *
Linkage with Universities & Research
Institutes
Universities (Uni) 0.276 * 0.058
Research Institutions (RI) 0.129 0.032
Knowledge Flows and Exchanges
Explicit Knowledge on Technology 0.373 ** 0.254
(Explicit1)
Explicit Knowledge on Market 0.538 *** 0.749 ***
(Explicit2)
Tacit Knowledge on Best Practices 0.356 ** -0.248
(Tacit)
The Role of Internal & External
Research
In-house R and D (Internal) 0.999 *** 1.494 ***
Contracting-out Research (External) 0.543 *** 0.593 **
CONTROL VARIABLES
Firm Size (Size) 0.159 ** .083
Firm Age (Age) -0.040 0.111
Industry
ICT and Communication Equipment 0.488 0.561
Aeronautics and Space -0.198 -1.597
Pharmaceuticals & Medical Devices 0.676
0.152
Construction (Materials, Equipment, -0.112 0.061
Heavy Construction)
Automotive 0.055 0.289
Metal Manufacturing -0.063 1.062 *
Plastics 0.452 0.309
Chemical Products -0.265 0.766
Textiles, Leather, Footwear 0.556 * 0.292
Energy 0.458 0.425
Production Equipment 1.041 ** 0.635 +
(Machinery, Electrical)
CONTROL VARIABLES
Food 0.227 0.620 (+)
Entertainment 0.529 0.211
Services 0.267 (+) 0.189
Industry Density (Density) 0.177 (+) 0.249 (+)
(Constant) -2.036 *** -2.288 ***
n 2297 1171
Chi-square 348.98 *** 204.84 ***
-2 Log likelihood 2068.69 1086.33
Cox & Snell R Square 16.3% 18.9%
Nagelkerke R Square 23.0% 25.8%
+ p < .10; * p < .05; ** p < .01; *** p < .001; Two-tailed
tests. Source: the Authors.
