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Open Innovation in SMEs: Trends, Motives and Management Challenges


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Open innovation has so far been studied mainly in high-tech, multinational enterprises. This exploratory paper investigates if open innovation practices are also applied by small-and medium-sized enterprises (SMEs). Drawing on a database collected from 605 innovative SMEs in the Netherlands, we explore the incidence of and apparent trend towards open innovation. The survey furthermore focuses on the motives and perceived challenges when SMEs adopt open innovation practices. Within the survey, open innovation is measured with eight innovation practices reflecting technology exploration and exploitation in SMEs. We find that the responding SMEs engage in many open innovation practices and have increasingly adopted such practices during the past 7 years. In addition, we find no major differences between manufacturing and services industries, but medium-sized firms are on average more heavily involved in open innovation than their smaller counterparts. We furthermore find that SMEs pursue open innovation primarily for market-related motives such as meeting customer demands, or keeping up with competitors. Their most important challenges relate to organizational and cultural issues as a consequence of dealing with increased external contacts.
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Technovation 29 (2009) 423–437
Open innovation in SMEs: Trends, motives and management challenges
Vareska van de Vrande
, Jeroen P.J. de Jong
, Wim Vanhaverbeke
Maurice de Rochemont
College of Management of Technology, Ecole Polytechnique Fe
´rale de Lausanne (EPFL), Odyssea 1.19, Station 5, 1015 Lausanne, Switzerland
EIM Business and Policy Research, The Netherlands
Faculty of Business Studies, Hasselt University, Belgium
Eindhoven University of Technology, The Netherlands
Open innovation has so far been studied mainly in high-tech, multinational enterprises. This exploratory paper investigates if open
innovation practices are also applied by small- and medium-sized enterprises (SMEs). Drawing on a database collected from 605
innovative SMEs in the Netherlands, we explore the incidence of and apparent trend towards open innovation. The survey furthermore
focuses on the motives and perceived challenges when SMEs adopt open innovation practices. Within the survey, open innovation is
measured with eight innovation practices reflecting technology exploration and exploitation in SMEs. We find that the responding SMEs
engage in many open innovation practices and have increasingly adopted such practices during the past 7 years. In addition, we find no
major differences between manufacturing and services industries, but medium-sized firms are on average more heavily involved in open
innovation than their smaller counterparts. We furthermore find that SMEs pursue open innovation primarily for market-related
motives such as meeting customer demands, or keeping up with competitors. Their most important challenges relate to organizational
and cultural issues as a consequence of dealing with increased external contacts.
r2008 Elsevier Ltd. All rights reserved.
Keywords: Open innovation; SMEs; Technology markets; Incidence; Perceived trend; Motives; Managerial challenges
1. Introduction
Open innovation has been proposed as a new paradigm
for the management of innovation (Chesbrough, 2003;
Gassmann, 2006). It is defined as ‘the use of purposive
inflows and outflows of knowledge to accelerate internal
innovation, and to expand the markets for external use of
innovation, respectively.’ (Chesbrough et al., 2006, p. 1). It
thus comprises both outside-in and inside-out movements
of technologies and ideas, also referred to as ‘technology
acquisition’ and ‘technology exploitation’ (Lichtenthaler,
Open innovation has received increasingly attention in
scientific research, but so far it has mainly been analyzed in
large, high-tech multinational enterprises (MNEs) drawing
on in-depth interviews and case studies (e.g. Chesbrough,
2003;Kirschbaum, 2005). Few studies have demonstrated
that open innovation also exists in smaller organizations.
Moreover, all of them focus on very specific industries, for
example open source software (Henkel, 2006) or tabletop
role-playing games (Lecocq and Demil, 2006). Whenever
large samples of enterprises are explored, the focus is on
specific issues rather than the full open innovation model
(e.g. Laursen and Salter, 2006;Chesbrough, 2002). To our
knowledge, only Lichtenthaler (2008) has so far attempted
to empirically study the incidence of open innovation in a
broader sample of enterprises. He focused on medium-
sized and large manufacturers in Germany, Switzerland
and Austria, as small enterprises and service industries
were not surveyed.
This study addresses this gap by focusing on small- and
medium-sized enterprises (SMEs). It is a first, explorative
study measuring to which extent SMEs apply open
0166-4972/$ - see front matter r2008 Elsevier Ltd. All rights reserved.
Corresponding author. Tel.: +41 21 693 0048; fax: +41 21 693 0020.
E-mail addresses: (V. van de Vrande), (J.P.J. de Jong),
(W. Vanhaverbeke), (M. de Rochemont).
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innovation practices and whether there is a trend towards
increased adoption of the open innovation model over
time. In doing so, we develop and test propositions on the
differences between manufacturing and services firms, and
between medium-sized and small enterprises. Furthermore,
we explore the motives of SMEs to engage in open
innovation and perceived management challenges in
implementing open innovation. To our knowledge this
study is the first to investigate the incidence of open
innovation in a broad sample of SMEs. In doing so, it
assesses whether open innovation is a trend that is not only
relevant for high-tech MNEs but also for a broader range
of firms and businesses. As we draw on a survey database
of 605 SMEs in the Netherlands, the paper also accounts
for the potential criticism that open innovation has so far
been studied mainly in American enterprises (e.g. Ches-
brough, 2003;Chesbrough and Crowther, 2006;Lecocq
and Demil, 2006) and not in others parts of the world
(a notable exception is Lichtenthaler, 2008).
The remainder of the paper is structured as follows.
Section 2 discusses open innovation and the dimensions of
technology exploitation and exploration that can be used
to classify open innovation practices. Next, we develop
some tentative propositions on the adoption of open
innovation practices in manufacturing and service firms,
and between different size categories of SMEs. Section 4
describes our data, while Section 5 analyses the incidence
and trend towards open innovation, and motives and
hampering factors of SMEs. Finally, Section 6 concludes
and discusses the limitations and implications of our work.
2. Open innovation
Traditionally, large firms relied on internal R&D to
create new products. In many industries, large internal
R&D labs were a strategic asset and represented a
considerable entry barrier for potential rivals. As a result,
large firms with extended R&D capabilities and comple-
mentary assets could outperform smaller rivals (Teece,
1986). This process in which large firms discover, develop
and commercialize technologies internally has been labeled
the closed innovation model (Chesbrough, 2003). Although
this model worked well for quite some time, the current
innovation landscape has changed. Due to labor mobility,
abundant venture capital and widely dispersed knowledge
across multiple public and private organizations, enter-
prises can no longer afford to innovate on their own, but
rather need to engage in alternative innovation practices.
As a result, a growing number of MNEs has moved to an
open innovation model in which they employ both internal
and external pathways to exploit technologies and, con-
currently, to acquire knowledge from external sources
(Chesbrough, 2003).
Open innovation is a broad concept encompassing
different dimensions. Following the definition mentioned
earlier, most studies distinguish between purposive out-
flows and inflows of knowledge to accelerate internal
innovation processes and to better benefit from inno-
vative efforts, respectively (e.g. Chesbrough et al., 2006;
Chesbrough and Crowther, 2006). Purposive outflows of
knowledge, or technology exploitation, implies innovation
activities to leverage existing technological capabilities
outside the boundaries of the organization. Purposive
inflows, which we will refer to as technology exploration,
relates to innovation activities to capture and benefit from
external sources of knowledge to enhance current techno-
logical developments. In a fully open setting, firms combine
both technology exploitation and technology exploration
in order to create maximum value from their technological
capabilities or other competencies (Chesbrough and
Crowther, 2006;Lichtenthaler, 2008).
2.1. Technology exploitation
In order to better profit from internal knowledge,
enterprises may engage in various practices. In this paper,
three activities related to technology exploitation will be
distinguished: venturing, outward licensing of intellectual
property (IP), and the involvement of non-R&D workers in
innovation initiatives.
Venturing is defined here as starting up new organiza-
tions drawing on internal knowledge, i.e. it implies spin-off
and spin-out processes. Support from the parent organiza-
tion may also include finance, human capital, legal advice,
administrative services, etc. Previous open innovation
studies have primarily focused on venturing activities in
large enterprises (e.g. Chesbrough, 2003;Lord et al., 2002).
The potential of venturing activities is regarded to be
enormous, e.g. Chesbrough (2003) illustrated that the total
market value of 11 projects which turned into new ventures
exceeded that of their parent company, Xerox, by a factor
of two.
IP plays a crucial role in open innovation as a result
of the in- and outflows of knowledge (Arora, 2002;
Chesbrough, 2003, 2006;Lichtenthaler, 2007). Enterprises
have opportunities to out-license their IP to obtain more
value from it (Gassmann, 2006). Out-licensing allows them
to profit from their IP when other firms with different
business models find profitable, external paths to the
market. The decision of firms to license out depends on
anticipated revenues and profit-dissipation effects (Arora
et al., 2001), i.e. outward licensing generates revenues in
the form of licensing payments, but current profits
might decrease when licensees use their technology to
compete in the same market. Prior research has shown the
importance of establishing a reputation as a knowledge
provider in order to increase the monetary and strategic
benefits of technology out-licensing (Lichtenthaler and
Ernst, 2007).
A third practice to benefit from internal knowledge is to
capitalize on the initiatives and knowledge of current
employees, including those who are not employed at the
internal R&D department. Several case studies illustrate
that informal ties of employees with employees of other
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organizations are crucial to understand how new products
are created and commercialized (e.g. Chesbrough et al.,
2006). Many practitioners and scientists, also outside the
field of open innovation, endorse the view that innovation
by individual employees is a means to foster organizational
success (e.g. Van de Ven, 1986). Work has become more
knowledge-based and less rigidly defined. In this context,
employees can be involved in innovation processes in
multiple ways, for example by taking up their suggestions,
exempting them to take initiatives beyond organizational
boundaries, or introducing suggestion schemes such as idea
boxes and internal competitions (e.g. Van Dijk and Van
den Ende, 2002).
2.2. Technology exploration
Technology exploration refers to those activities which
enable enterprises to acquire new knowledge and techno-
logies from the outside. In the survey, five practices were
distinguished related to technology exploration: customer
involvement, external networking, external participation,
outsourcing R&D and inward licensing of IP.
Open innovation theorists recognize that customer
involvement is one important alternative to inform internal
innovation processes (Gassmann, 2006). Drawing on the
work of Von Hippel (2005) users are increasingly regarded
not as just passive adopters of innovations, but they may
rather develop their own innovations which producers can
imitate. Users for example regularly modify their current
machines, equipment and software to better satisfy process
needs, and because producers fail to provide an adequate
supply (Von Hippel, 2005). Firms may benefit from their
customers’ ideas and innovations by proactive market
research, providing tools to experiment with and/or
develop products similar to the ones that are currently
offered, or by producing products based on the designs of
customers and evaluating what may be learned from
general product development.
External networking is another important dimension
which is consistently associated with open innovation
(Chesbrough et al., 2006). It includes all activities to
acquire and maintain connections with external sources of
social capital, including individuals and organizations. As
such, it comprises both formal collaborative projects and
more general and informal networking activities. Networks
allow enterprises to rapidly fill in specific knowledge needs
without having to spend enormous amounts of time and
money to develop that knowledge internally or acquire it
through vertical integration. Networks may also evolve
into formal collaborative efforts such as R&D alliances.
Such alliances between non-competing firms have become a
popular vehicle for acquiring technological capabilities
(Gomes-Casseres, 1997).
External participations enable the recovery of innova-
tions that were initially abandoned or that did not seem
promising. Enterprises may invest in start-ups and other
businesses to keep an eye on potential opportunities
(Chesbrough, 2006;Keil, 2002). Such equity investments
provide opportunities to further increase external colla-
boration in case their technologies prove to be valuable
(Van de Vrande et al., 2006). Enterprises may also
outsource R&D activities to acquire external knowledge.
At the heart of the open paradigm is the assumption that
enterprises cannot conduct all R&D activities by them-
selves, but instead have to capitalize on external knowledge
which can be licensed or bought (Gassmann, 2006).
Technical service providers such as engineering firms and
high-tech institutions have also become more important in
the innovation process. In the open model it is considered
fully legitimate to bring key knowledge development
outside the organizational boundary (e.g. Prencipe, 2000).
Finally, enterprises can externally acquire intellectual
property, including the licensing of patents, copyrights or
trade marks, to benefit from external innovation opportu-
nities (Chesbrough, 2006). This may be a necessity to fuel
one’s business model and to speed up and nurture internal
research engines.
To conclude, in comparison with the closed model, the
open innovation model implies that the management and
organization of innovation processes becomes more com-
plex, i.e. open innovation includes many more activities
than just those that were assigned to a traditional R&D
3. Innovation in SMEs
Having discussed how open innovation can be defined
and operationalized, the current section develops some
tentative propositions on the incidence of open innovation
in SMEs, what differences can be anticipated between
industries and size classes, and what motives and manage-
ment challenges may be encountered.
3.1. Incidence and trends
In the closed innovation model enterprises must generate
their own ideas and then develop, build, market, distribute,
and support them on their own. This model counsels
enterprises to be strongly self-reliant, implicitly recom-
mending organizing innovation in internal R&D depart-
ments. In contrast, the open model prescribes enterprises to
draw on both external and internal ideas and paths to the
market, when enterprises look to discover and develop
innovative opportunities (Chesbrough, 2003). In doing so,
the open innovation model recognizes that smaller firms
take an increasingly prominent role in the contemporary
innovation landscape. Some first tentative evidence is
found in Chesbrough (2003) as he cited statistics of how
small enterprises contribute to total industrial R&D
expenses in the US. They accounted for around 24% of
all R&D spending in 2005, compared to only 4% in 1981
(National Science Foundation, 2006). Likewise, in an
interview-based study of 12 enterprises in mainly low-tech
industries, Chesbrough and Crowther (2006) found that
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basically all respondents had to some extent picked up
open innovation practices, with a clear focus on technology
exploration activities. Another example is Lichtenthaler
(2008) who conducted a survey among medium-sized and
large manufacturers in Germany, Austria and Switzerland.
He found that 32.5% of the respondents was somehow
engaged in open innovation.
Besides, there have been multiple studies on the strengths
and weaknesses of SMEs in their organization of innova-
tion processes (e.g. Vossen, 1998;Acs and Audretsch,
1990). This work concludes that innovation in SMEs is
hampered by lack of financial resources, scant opportu-
nities to recruit specialized workers, and small innovation
portfolios so that risks associated with innovation cannot
be spread. SMEs need to heavily draw on their networks to
find missing innovation resources, and due to their
smallness, they will be confronted with the boundaries of
their organizations rather sooner than later. In today’s
increasingly complex and knowledge-intensive world
with shortened product life cycles, such networking
behavior has become probably even more important
than before. Given these considerations, we anticipate that
open innovation practices are not exclusively applied by
MNEs, but will also be present in SMEs, and will be
increasingly adopted.
3.2. Industries and size classes
Prior research gives the impression of industrial differ-
ences regarding the incidence of and trend towards open
innovation. In the current paper, we explore the differences
between manufacturing and services industries. Services
differ from physical goods in terms of intangibility,
inseparability, heterogeneity and perishability (Atuahene-
Gima, 1996). Given the distinct nature of the offerings of
manufacturing and services firms, differences in the
adoption of open innovation may be very plausible. As
physical goods are more separable and homogenous, it is
much easier to outsource parts of the R&D process or to
in-source new ideas and technologies that fit with current
business lines. Gassmann (2006) proposes that industries
are more prone to engage in open innovation if they are
characterized by globalization, technology intensity, tech-
nology fusion, new business models and knowledge
leveraging. We argue that especially the first three
characteristics as defined by Gassmann (2006) are more
applicable to manufacturers than to services enterprises,
i.e. manufacturing enterprises generally tend to operate in
larger geographical regions and the nature of their
processes demands higher investments in capital and
technologies. For services—due to their relatively intangi-
ble, simultaneous and heterogeneous nature—the opposite
applies. Indeed, descriptive statistics of Dutch enterprises
offered by Statistics Netherlands (2006) demonstrate that
manufacturers are on average more technology-intensive,
invest more in R&D, and operate in larger regions. We
therefore anticipate that the incidence and adoption of
open innovation will be stronger in manufacturing
Besides industry differences, the size of enterprises may
also influence the adoption of open innovation. Our survey
results contain information on both small enterprises
(defined as 10–99 employees) and medium-sized ones
(100–499 employees). Past work has shown that there is a
great deal of difference in the innovation strategies of small
and large firms (e.g. Vossen, 1998;Acs and Audretsch,
1990). Innovation processes of larger firms are typically
more structured and professionalized. As SMEs grow they
increasingly develop and apply formal structures, also
marked by recruiting specialized workers, and introducing
managerial layers, rules and procedures (Greiner, 1972).
Once a critical size is reached, they may be better able to
formalize their innovation practices and to develop
structures for licensing IP, venturing activities and external
participations. Their larger size also enables them to
maintain large and diversified innovation portfolios
(to spread risks) and to reserve structural funds to finance
innovation. This may have important implications for
the application of open innovation in these firms. The
extent to which they can engage in technology exploitation
and exploration activities is likely to be contingent on
their size. As a result, we propose that open innovation
is more commonly applied by medium-sized enterprises
and that any trend towards open innovation is stronger in
this group.
3.3. Motives and challenges
Many firms started to implement open innovation as a
necessary organizational adaptation to changes in the
environment (Chesbrough, 2003). In a world of mobile
workers, abundant venture capital, widely distributed
knowledge and reduced product life cycles, most enter-
prises can no longer afford to innovate on their own. A
further exploration of motives was done by Chesbrough
and Crowther (2006). In an interview-based study they
found that the most common reason for external technol-
ogy acquisition was a common belief that it is critical to
maintain growth. It is anticipated that basic entrepreneur-
ial values such as growth and revenues will be among the
key motives of enterprises to practice open innovation.
Previous work on motives for open innovation focused on
MNEs and usually covered only few open innovation
practices. EIRMA (2003) for example showed that R&D
managers of large corporations engage in venturing for
market-related motives such as meeting customer demands,
but also to acquire new knowledge. Likewise, Jacobs and
Waalkens (2001) found that organizations ‘innovate their
innovation processes’ to reduce time-to-market and to
better utilize internal creativity. Hence, we expect market
considerations and knowledge creation to be key motives
for open innovation.
Other potential motives can be derived from inno-
vation collaboration studies. This literature suggests that
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enterprises may engage in collaboration to acquire missing
knowledge, complementary resources or finance, to spread
risks, to enlarge its social networks, or to reduce costs
(Hoffman and Schlosser, 2001;Mohr and Spekman, 1994).
Such motives mainly reflect outside-in considerations while
motives to conduct outbound activities seem to be missing.
Koruna (2004) however identified various objectives for
firms to externally exploit their knowledge, including
revenues and access to knowledge, but also to set industry
standards, to profit from infringements, to realize learning
effects, and to guarantee freedom to operate by establish-
ing cross-licensing agreements with other organizations.
As for the challenges of open innovation in SMEs, our
data set contains information on perceived barriers to
adopt open innovation practices. The open innovation
literature has so far witnessed few attempts to explore this
subject. Chesbrough and Crowther (2006) for example
identified the not-invented-here (NIH) syndrome and lack
of internal commitment as main hampering factors. The
NIH syndrome has been previously found to be a
prominent barrier for external knowledge acquisition (e.g.
Katz and Allen, 1982). Although focused on the external
acquisition of knowledge, its underlying antecedents are
also applicable to technology exploitation, leading to the
‘only-used-here’ (OUH) syndrome (Lichtenthaler and
Ernst, 2006). More potential barriers can again be found
in the related literature on collaborative innovation.
Boschma (2005) for example identified various forms of
‘proximity’ which are essential for effective collaboration.
These include cognitive, organizational, cultural and
institutional differences between collaboration partners,
implying that potential problems may arise due to
insufficient knowledge, cultures or modes of organization,
or bureaucratic elements. To mention only a few, other
potential barriers include lacking resources, free-riding
behavior, and problems with contracts (Hoffman and
Schlosser, 2001;Mohr and Spekman, 1994).
4. Methods
4.1. Sample
To analyze the trends, motives and management
challenges of SMEs with regards to open innovation, we
use a survey database that was collected by EIM, a Dutch
institute for business and policy research. The survey was
commissioned by the Dutch Advisory Council on Science
and Technology to support a policy advice on open
innovation (see AWT, 2006). The survey targeted SMEs,
defined as enterprises with no more than 500 employees,
and was implemented by means of computer-assisted
telephone interviewing. Data collection was done over a
3-week period in December 2005. To reliably identify
trends only respondents with long tenure and representing
enterprises that systematically innovate, were selected. The
survey therefore started with screening questions. Respon-
dents first indicated if their company had developed at least
one innovation in the past 3 years. This could either be a
product-, process-, organizational- or marketing-related
innovation as defined by the Oslo manual (a set of integral
guidelines for the collection of innovation data, see OECD,
2005). Secondly, the survey asked if respondents’ enter-
prises had formulated an innovation strategy. Thirdly,
respondents had to be employed in their current jobs for at
least 7 years. In this way, the screening ensured that
respondents all represented SMEs with systematic innova-
tion efforts, and they were in a position to adequately judge
if and how innovation processes had developed over the
past 7 years.
The sample was disproportionally stratified across
manufacturing and service industries and two size classes
(10–99 employees and 100–499 employees). Enterprises with
less than 10 employees (micro-enterprises) were excluded
since they generally have no or limited identifiable innova-
tion activities, and this population usually contains many
start-ups. It was anticipated that very few micro-enterprises
would pass the screening. The sample was drawn from the
Dutch Chambers of Commerce database. Interviewers
explicitly asked for those who were responsible for
innovation, i.e. small business owners, general managers,
R&D managers or staff managing new business develop-
ment activities. In total 2230 respondents were contacted, of
whom 1206 persons (54%) were willing to participate.
To check for non-response bias, respondents and non-
respondents were compared across industries and size
classes. Contrasting both groups with w
-tests revealed no
significant differences at the 5% level (p¼0.23 for type of
industry and p¼0.55 for size classes). A total of 605
respondents passed the screening phase, corresponding with
afinalsamplingrateof27%.Table 1 shows how these res-
pondents are distributed across size classes and industries.
4.2. Variables
The survey proceeded with questions on the nature of
firms’ innovation processes. More specifically, eight open
innovation practices were distinguished, which are defined
in Table 2.
After the screening questions, the respondents were
asked if their enterprise had engaged in any venturing
activities in the past 3 years. Throughout the survey a time
space of 3 years was used, resembling the criterion used by
Statistics Netherlands to identify innovative enterprises
(Statistics Netherlands, 2006;OECD, 2005). Secondly,
respondents were asked if venturing activities in their
enterprise had increased, remained stable, or had decreased
in the past 7 years (if venturing activities were missing the
question was rephrased as if venturing had been stable or
decreased). Thirdly, in case the firms were involved in open
innovation activities, the interviewer asked to provide the
motives to do so. Their answers were recorded in an open-
ended format. Finally, respondents were asked if they had
perceived any barriers to implement open innovation
practices, and if so, to describe them.
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The other innovation practices were surveyed with an
identical sequence of questions. The only exception was the
innovation practice of outward IP licensing. Here, the
sequence was preceded by a screening question checking
whether the firm actually possessed any IP.
During the third and fourth part of the survey,
respondents were asked to clarify their motives when they
get involved in the different ‘open innovation’ practices.
The various answers of the respondents to the question
what drives them to get involved in open innovation
practices were coded, resulting in the categories described
in Table 7. A similar approach was adopted for the
perceived barriers to adapt open innovation. This resulted
in elf types of barriers described in Table 8. The coding
process was organized with two researchers. They first read
all open-ended answers and together identified a number of
preliminary categories. Next, they carefully studied all
answers and classified them into the scheme. New
categories could be proposed whenever they felt that the
categories were insufficient or should be refined. Finally, all
classifications were compared and different opinions
discussed and resolved. Because only few SMEs possess
and trade IP (see Table 3), the data did not contain enough
records to provide reliable insights about respondents’
motives and challenges on this topic.
5. Results
5.1. Incidence and trends
Table 3 shows the incidence of open innovation practices
in our sample of innovative SMEs. The three last columns
also give an overview of the evolution of the use of these
practices in Dutch innovative SMEs. The table shows the
shares of respondents conducting various aspects of
technology exploitation and technology exploration, and
the extent to which they perceived an increase, stabilization
or decrease in the application of these practices in the
past 7 years.
Table 3 shows that customer involvement, external
networking and employee involvement are fairly common
innovation practices. Outward and inward licensing of IP,
venturing and external participations in other enterprises
are conducted by only by a minority of the respondents,
while R&D outsourcing is done by half of the sample.
Table 1
Distribution of respondents across industries and size classes
Type of industry Size class
10–99 employees 100–499 employees Total
Food and beverages (NACE codes 15–16) 40 21
Chemicals, rubber and plastics (NACE codes 23–25) 54 22
Machinery and equipment (NACE codes 29–34) 19 32
Other manufacturers (NACE codes 17–22; 26–28; 35–37) 47 53
160 128 288
IT (NACE code 72) 53 17
Business services (NACE codes 73–74) 59 24
Other services (NACE codes 50–71; 93) 104 60
216 101 317
Total 376 229 605
Table 2
Surveyed open innovation practices
Practice Definition
Technology exploitation
Venturing Starting up new organizations drawing on internal
knowledge, and possibly also with finance, human
capital and other support services from your enterprise.
Outward IP
Selling or offering licenses or royalty agreements to
other organizations to better profit from your
intellectual property, such as patents, copyrights or
trade marks.
Leveraging the knowledge and initiatives of employees
who are not involved in R&D, for example by taking up
suggestions, exempting them to implement ideas, or
creating autonomous teams to realize innovations.
Technology exploration
Directly involving customers in your innovation
processes, for example by active market research to
check their needs, or by developing products based on
customers’ specifications or modifications of products
similar like yours.
Drawing on or collaborating with external network
partners to support innovation processes, for example
for external knowledge or human capital.
Equity investments in new or established enterprises in
order to gain access to their knowledge or to obtain
others synergies.
Buying R&D services from other organizations, such as
universities, public research organizations, commercial
engineers or suppliers.
Inward IP
Buying or using intellectual property, such as patents,
copyrights or trade marks, of other organizations to
benefit from external knowledge.
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The table also shows that for every surveyed practice, the
share of respondents perceiving an increase over the past 7
years is substantially larger than the share with a decrease.
These results suggest that open innovation is not just
conducted by MNEs, but rather also applies to a broad
sample of SMEs, and moreover, open innovation is on
average increasingly adopted.
5.2. Industries and size classes
Table 4 compares the incidence and trend towards open
innovation between manufacturing and services enter-
prises. For ease of presentation, trend scores have been
averaged. We applied various tests to analyze significant
differences. As t-test procedures were less suitable because
most dependent variables violated the required normal
distribution, Table 4 reports non-parametric Mann–
Whitney tests on significant median differences. We did
routinely check if our results were robust for the chosen
test. It appeared that w
- and independent samples t-tests
produced nearly identical results. Besides, as Dutch
manufacturers tend to be relatively large organizations
(Bangma, 2005), we also ran multivariate analysis of
variance models in which size classes were entered as
control variables. Again, significances of the differences
between manufacturing and services were nearly identical
(output available on request).
The left-hand side of Table 4 shows only few significant
differences between manufacturing and services enter-
prises. Employee involvement, customer involvement and
external networking appear to be main types of open
innovation conducted by both manufacturers and services
enterprises. We do remark that these practices were defined
very broadly (Table 2) and hence may blur any significant
difference (also see discussion section). Nevertheless, the
other indicators reveal no systematic pattern of differences
between industries. In manufacturing there seems to be
somewhat more attention for technology exploration, i.e.
manufacturers relatively often engage in R&D outsourcing
and inward IP licensing. In contrast, services enterprises do
better on venturing activities (33% versus 24%, po0.05).
The right-hand side of Table 4 reveals that the trend
towards open innovation is observed in both industries, i.e.
average trend scores are consistently positive. We again
find only few significant differences. Manufacturers have
adopted R&D outsourcing more often (0.23 versus 0.13,
po0.01) while the opposite applies to venturing activities.
In a recent survey of manufacturers, Lichtenthaler (2008)
analyzed industry differences in more detail and also found
no significant differences. In all, we do not find major
differences between the manufacturing and services in-
dustries with regards to the incidence and trend towards
open innovation practices.
Table 5 provides similar output for the differences
between small- and medium-sized enterprises. Again,
significances were analyzed with different tests (including
multivariate analysis of variance with industry controls)
and proved to be robust.
Table 3
Incidence and perceived trends in open innovation practices (n¼605)
Perceived trend
Technology exploitation
Venturing 29 14 84 2
Outward IP licensing 10 4 95 1
Employee involvement 93 42 57 1
Technology exploration
Customer involvement 97 38 61 1
External networking 94 29 67 4
External participation 32 16 83 1
Outsourcing R&D 50 22 73 5
Inward IP licensing 20 5 93 2
Table 4
Incidence of and perceived trends in open innovation practices between industries
Incidence Perceived trend
(n¼288) (%)
(n¼317) (%)
Technology exploitation
Venturing 24 33 2.4
0.09 0.15 2.1
Outward IP licensing 11 8 1.2 0.02 0.02 0.1
Employee involvement 94 93 0.7 0.41 0.41 0.2
Technology exploration
Customer involvement 98 97 0.8 0.34 0.40 1.3
External networking 95 94 0.6 0.24 0.26 0.4
External participation 29 34 1.2 0.14 0.15 0.3
Outsourcing R&D 59 43 4.0** 0.23 0.13 2.6*
Inward IP licensing 25 15 3.2* 0.04 0.03 0.6
**po0.001, *po0.01,
Average score with increase coded 1, stable coded 0 and decrease coded 1.
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Table 5 shows that medium-sized enterprises (100–499
employees) are more likely to engage in open innovation.
On all technology exploitation and exploration practices
they are doing slightly or substantially better. Bearing in
mind that employee involvement, customer involvement
and external networking were broadly defined, the
differences between both size classes are not significant.
As for perceived trends, the right-hand side of Table 5
shows substantial differences. All values in the column of
respondents with 100–499 employees are (much) larger.
Especially for the technology exploration activities med-
ium-sized enterprises are much more involved in these open
innovation activities. This result contrasts the findings by
Lichtenthaler (2008), who concluded that firm size did not
have a major impact on the degree of technology
exploration, but it did influence technology exploitation.
In sum, we find that medium-sized enterprises apply and
adopt open innovation more often than their smaller
counterparts, as expected.
5.3. Cluster analysis
To explore the incidence of open innovation in more
detail, we decided to cluster the respondents in groups of
SMEs that are homogenous in their open innovation
strategy and organization of innovation practices (see also
Lichtenthaler (2008) for a similar approach). The analysis
was based on the eight dichotomous variables measuring
the incidence of technology exploitation and exploration
practices. We started the analysis with a principal
component analysis (PCA) to reduce the number of
dimensions in our data and applied cluster analytic
techniques to find homogeneous groups of enterprises.
Finally, the differences between clusters were explored with
non-parametric tests.
PCA summarizes the variance of a set of variables in a
limited number of components. This provides uncorrelated
component scores at the interval level which are more
suitable for cluster procedures, and prevents that single
variables dominate a cluster solution (Hair et al., 1998). A
first exploratory run demonstrated that our data were
suitable for PCA (i.e. MSA values all 40.57, KMO
measure ¼0.61 and p(Bartlett)o0.001, see Hair et al.,
1998). To determine the number of components we applied
the latent root criterion (eigenvalues 41.0). As a result we
obtained a three-dimensional solution explaining 57% of
the variance. In the appendix of this paper, the matrix of
component loadings is shown. The first component reflects
the practices of employee involvement, external involve-
ment and external networking. The second component
contains R&D outsourcing and outward and inward IP
licensing. The third one relates to venturing and external
participation. Since the PCA was done to reduce the
number of dimensions, we did not attempt to label these
components, but instead used the three factor scores as a
basis for our cluster exercise.
In the cluster analysis we combined hierarchical and
non-hierarchical techniques. This helps to obtain more
stable and robust taxonomies (Milligan and Sokol, 1980;
Punj and Stewart, 1983). The hierarchical analysis was
done with Ward’s method based on squared Euclidian
distances. Next, non-hierarchical cluster analyses were
done to determine a final solution. We considered a range
of initial solutions from the hierarchical analysis with either
Table 5
Incidence of and perceived trends in open innovation practices between size classes
Incidence Perceived trend
10–99 employees
(n¼376) (%)
100–499 employees
(n¼229) (%)
10–99 employees
100–499 employees
Technology exploitation
Venturing 27 32 1.4 0.11 0.14 1.2
Outward IP
6 16 4.3** 0.01 0.04 1.5
92 96 1.7 0.37 0.48 2.8*
Technology exploration
97 98 1.1 0.30 0.50 4.6**
94 95 0.4 0.20 0.33 3.2*
24 44 5.2** 0.13 0.18 2.0
42 64 5.1** 0.14 0.24 2.5
Inward IP
14 29 4.7** 0.02 0.07 2.2
**po0.001, *po0.01,
Average score with increase coded 1, stable coded 0 and decrease coded 1.
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two, three, four or five groups (as suggested by the
dendogram). For each number of groups (k), we performed
ak-means non-hierarchical analysis, in which SMEs were
iteratively divided to the groups based on their distance to
the centroids of our initial hierarchical solutions for
(following Milligan and Sokol, 1980;Punj and Stewart,
1983). To assess which solution was most stable we
computed kappa, the chance corrected coefficient of
agreement (Singh, 1990), between each initial and final
solution. The three-cluster solution appeared to be optimal
(k¼0.95, while ko0.94 for the other solutions).
A basic validity requirement is that one should find
significant differences between the variables used to
develop the clusters (Hair et al., 1998). Kruskal–Wallis
tests confirmed this for all variables (Table 6). Again, all
significances reported here are robust, i.e. either parametric
or non-parametric tests give identical results.
Firms in cluster 1 are most strongly involved in open
innovation. They use a broad set of innovation practices to
improve their innovation performance and are on average
larger and are relatively more based in manufacturing
industries compared to the other two clusters. Cluster 2 is
the largest group of firms; these enterprises nearly always
rely on the involvement of employees and customers, and
external networking, features which are shared with cluster 1.
Cluster 3 includes innovative firms that rely heavily on
customer involvement but most of them are not involved in
relatively complex and formalized transaction forms of
open innovation activities such as venturing, IP-trading,
outsourcing of R&D and participation in other firms. The
clusters provide a similar view on how SMEs apply open
innovation practices as was earlier identified by Lich-
tenthaler (2008) for medium-sized and large manufac-
turers. Most enterprises have adopted either open or closed
strategies on both technology exploration and exploitation
activities, i.e. only few respondents are found with
decidedly high scores on one dimension and low scores
on the other, and there are not sufficient of them to form
separate clusters.
To further explore the differences between clusters,
Table 7 compares average trend scores for the application
of innovation practices in the past 7 years. Respondents
in cluster 1, which are strongly embracing open innovation,
also intensified the adoption of the open model the
most. The opposite applies to the third cluster. In other
words, the differences between the three clusters are
growing over time. Nevertheless, there is a trend towards
increased adoption of open innovation in all clusters;
only inward IP licensing is becoming less popular in the
third cluster.
Table 6
Incidence of open innovation practices across three clusters
Cluster1 (n¼133) (%) Cluster2 (n¼411) (%) Cluster3 (n¼61) (%) Kruskal-Wallis w
(df ¼2)
Technology exploitation
Venturing 40 27 15 14.5*
Outward IP licensing 44 1 0 227.3**
Employee involvement 98 99 38 340.5**
Technology exploration
Customer involvement 98 99 77 109.3**
External networking 99 100 44 310.2**
External participation 44 31 11 20.4**
Outsourcing R&D 70 48 21 41.5**
Inward IP licensing 86 0 5 486.9**
**po0.001, *po0.01,
Table 7
Perceived trend
in open innovation practices across three clusters
Cluster1 (n¼133) Cluster2 (n¼411) Cluster3 (n¼61) Kruskal-Wallis w
(df ¼2)
Technology exploitation
Venturing 0.17 0.11 0.05 5.2
Outward IP licensing 0.11 0.00 0.00 26.0**
Employee involvement 0.53 0.43 0.07 36.1**
Technology exploration
Customer involvement 0.52 0.38 0.05 36.3**
External networking 0.29 0.27 0.05 11.5*
External participation 0.23 0.14 0.02 14.6*
Outsourcing R&D 0.21 0.18 0.07 4.9
Inward IP licensing 0.17 0.00 0.03 47.4**
**po0.001, *po0.01,
Average score with increase coded 1, stable coded 0 and decrease coded 1.
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We also investigated if enterprises in the three clusters
are evenly distributed across industries and size classes (see
Table 6). As for industries, 58% of the respondents in
cluster 1 are manufacturing companies. In clusters 2 and 3
these percentages are 55 and 43, respectively. A Kruskal–
Wallis test shows that these differences are significant at
po0.05 (Kruskal–Wallis w
¼7.3, df ¼2). Focusing on
size classes, 55% of the respondents in cluster 1 are
medium-sized enterprises. In clusters 2 and 3 these shares
are 34% and 25%, respectively. Again, the differences are
significant, now at po0.001 (Kruskal–Wallis w
df ¼2). It thus appears that enterprises in cluster 1 (open
innovators) tend to be larger organizations. These results
suggest a sequence in the adoption of open innovation
practices as organizations grow. Cluster 3 contains many
small enterprises with modest application of open innova-
tion, but even here a majority of firms involves customers
in their innovation processes. The most distinctive feature
of cluster 2 is that these SMEs all engage in practices which
can be organized informally and which do not necessarily
require substantial investments, including employee invol-
vement and external networking. Medium-sized enterprises
are clearly over-represented and their innovation activities
are also marked by practices which usually demand
substantial investments, including venturing, external
participations, IP licensing and R&D outsourcing.
5.4. Motives and challenges
The results analyzed in the previous section show that
SMEs clearly have taken up a more open approach
towards innovation. An important part of the survey
focused on the motives and challenges of SMEs when
pursuing open innovation. Table 8 shows that for almost
all open innovation practices pursued by SMEs, the most
important motives are market-related ones. For the
majority of respondents, using new innovation methods is
regarded as a way to keep up with market developments
and to meet customer demand, which eventually should
result in increased growth, better financial results, or
increased market share. Market-related motives are the
most important determinant for companies to engage in
venturing (31%), to participate in other firms (36%) and to
involve user in the innovation process (61%). Many SMEs
believe it is necessary to use a broad set of methods to meet
the ever-changing customer demand and to prevent the
firm from being outperformed by competitors or new
entrants. Motives related to control, focus, costs and
capacity are mentioned less frequently.
An important finding is that the different innovation
practices seem to have the same underlying motives. This
implies that venturing, participation in other firms, inter-
organizational networks and customer involvement are
Table 8
Motives to adopt open innovation practices
Category Examples Technology exploitation Technology exploration
(n¼256) (%)
(n¼232) (%)
(n¼175) (%)
(n¼94) (%)
(n¼134) (%)
Control Increased control over activities, better
organization of complex processes
19 1 1 3 1
Focus Fit with core competencies, clear focus of
firm activities
8– 1 1 3
Improved product development, process-/
market innovation, integration of new
23 – 19 21 24 8
Knowledge Gain knowledge, bring expertise to the
4– 5 35 6 44
Costs Cost management, profitability, efficiency 13 2 2 11 9
Capacity Cannot do it alone, counterbalance lack of
1– 3 7 5 13
Market Keep up with current market
developments, customers, increase growth
and/or market share
313 61 22 36 14
Utilization Optimal use of talents, knowledge,
qualities, and initiatives of employees
–30 –
Policy Organization principles, management
conviction that involvement of employees
is desirable
–15 –
Motivation Involvement of employees in the
innovation process increases their
motivation and commitment
–22 –
Other 19 11 9 11 14 8
Total 100 100 100 100 100 100
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complementary innovation activities in improving product
development, integrating new technologies and keeping up
with current market developments.
Employee involvement is the only item where motives
are different than for the other items. SMEs capitalize on
the knowledge and initiatives of their (non-R&D) employ-
ees for optimal use of human capital and for market
considerations. However, employee involvement is also the
outcome of an ‘internal organizational policy’ or it is
stimulated to improve motivation and commitment of
employees. These two motives are not necessarily dictated
by innovation objectives.
Table 9 identifies the main managerial and organiza-
tional challenges that SMEs perceive when they adopt open
innovation practices. We remind that interviewers first
asked if respondents had experienced any barriers to open
innovation. If respondents answered positively, the inter-
viewer explored the nature of these barriers by open-ended
questions. The main barriers to innovation mentioned by
the respondents are related to venturing (mentioned by
48% of the respondents), external participation (48%), and
outsourcing of R&D (43%).
Table 9 shows the extent to which the barriers mentioned
above matter for each of the different types of open innova-
tion activities. Organization and corporate culture-related
issues that typically emerge when two or more companies
are working together are clearly the most important
barriers/ that firms face when they engage in venturing
(35%), participation in other firms (75%), and the
involvement of external parties and users (resp. 48% and
30%). These types of open innovation require cooperation
among different organizations, or, in the case of ventur-
ing, employees who leave the organization. These inter-
organizational relationships frequently lead to problems
concerning the division of tasks and responsibility, the
balance between innovation and day-to-day management
tasks, and communication problems within and between
The availability of time and resources is another barrier.
This is a barrier for almost all types of open innovation
practices but the relatively low scores in Table 9 indicate
that time and resources are not the most important barriers
to implement open innovation practices. Administration-
related problems occur much more frequently, typically in
the context of venturing (28%), participation in other firms
(13%) and the involvement of external parties (10%), more
specifically when cooperating with governmental or other
not-for-profit institutions. Administrative burdens are also
prominent when the company receives governmental
subsidies and grants. Governmental support is experienced
Table 9
Hampering factors when adopting open innovation practices
Category Examples Technology exploitation Technology exploration
(n¼88) (%)
(n¼68) (%)
(n¼53) (%)
(n¼45) (%)
(n¼57) (%)
Administration Bureaucracy, administrative burdens,
conflicting rules
28 – 10 13 19
Finance Obtaining financial resources 10 5 4
Knowledge Lack of technological knowledge, competent
personnel, or legal/administrative knowledge
5– 5 –
Marketing Insufficient market intelligence, market
affinity, marketing problems of products
10 – 5
Balancing innovation and daily tasks,
communication problems, aligning partners,
organization of innovation
35 – 30 48 75 36
Resources Costs of innovation, time needed 5 17 10 7 10
IPR Ownership of developed innovations, user
rights when different parties cooperate
– – 10 5
Quality of
Partner does not meet expectations,
deadlines are not met
–– 24 – 28
Adoption Adoption problems, customer requirements
– – 14
Demand Customer demand too specific, innovation
appears not to fit the market
– – 28
Competences Employees lack knowledge/competences, not
enough labor flexibility
–24 –
Commitment Lack of employee commitment, resistance to
–51 –
Employees have too many ideas, no
management support
–8– – –
Other 7 – 8 1 2 3
Total 100 100 100 100 100 100
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as being highly inflexible, also because it is not allowed to
change partners and such programs cannot be ended
In addition, every single open innovation practice creates
its own specific problems. For instance, when companies
involve external parties in the innovation process, they
frequently report that these partners cannot meet the
expectations or deliver the required quality of a product or
a service. User involvement goes together with problems
related to property rights, adoption and too specific
customer demands. When relying on employees to imple-
ment open innovation, it often turns out that they do not
have the required capabilities or skills to make a valuable
contribution to innovation, or they lack motivation to do
so. It also happens that in the end, management decides not
to take up any of the ideas provided by employees or that
the number of ideas coming from individual employees just
gets too large to handle in an efficient way. This, in turn,
poses new challenges to managers when they want to get
the most out of the creativity of large numbers of
individuals. Eventually they can get assistance from a
growing number of specialized services firms to execute
this job.
Overall, we can conclude that many barriers for open
innovation in SMEs are related to corporate organization
and culture, no matter which type of open innovation is
pursued. On top of that, different types of open innovation
also have their own specific types of problems and barriers
to overcome. Remark also that the number of observations
in Table 9 is quite smaller than in Table 8. There are three
possible explanations for this observation: first, it can
indicate that many respondents did not experience any
barriers to implement open innovation practices; next,
respondents may not be aware of any barriers because they
cannot compare them with best practices; finally, respon-
dents were aware of some problems but could not
articulate them.
6. Discussion
6.1. Conclusions
Open innovation research has so far focused on large
and multinational enterprises (MNEs). Open innovation
practices in innovating SMEs have been neglected. This
study addresses this gap by exploring the incidence of and
trends towards open innovation in SMEs. Drawing on a
survey database of 605 innovative SMEs in the Nether-
lands, we conclude that SMEs are practicing extensively
open innovation activities, and, more importantly, that
they are increasingly doing so. In all, open innovation is
relevant and present in business life, i.e. it applies not just
to MNEs but also to a much broader group of small- and
medium-sized enterprises. Our results are in line with the
recent survey study of Lichtenthaler (2008) who demon-
strated that medium-sized and large manufacturers em-
brace open innovation practices.
Drawing on an existing database, open innovation was
operationalized along two dimensions, i.e. technology
exploitation (reflecting innovation practices to organize
purposive outflows of knowledge) and technology explora-
tion (purposive inflows of knowledge). For technology
exploitation, our data suggests that many SMEs attempt
to benefit from the initiatives and knowledge of their
(non-R&D) workers. For technology exploration, by far
most SMEs somehow try to involve their customers in
innovation processes by tracking their modifications in
products, proactively involving them in market research,
etc. This result confirms the importance of user innovation
(Von Hippel, 2005) for many SMEs: reducing the focus of
open innovation in SMEs to science-driven innovations
would seriously bias our understanding of open innovation
for this category of firms. Furthermore, external network-
ing to acquire new or missing knowledge is an important
open innovation activity among SMEs. In contrast, out-
ward and inward IP licensing, venturing activities and
external participations are only practiced by a minority of
the respondents. The more popular practices like customer
involvement and external networking are informal, un-
structured practices which do not necessarily require
substantial investments. IP licensing, venturing and ex-
ternal participation on the contrary, require financial
investments, formalized contracts and a structured innova-
tion portfolio approach to manage the risks. This finding is
in line with former studies about innovation in SMEs
(e.g. Vossen, 1998).
One of the major objectives of the survey was to know
whether open innovation is increasingly practiced by
SMEs during the last 7 years. Respondents unequivocally
perceive a trend towards increased popularity and dis-
semination of open innovation. Our findings suggest that
innovation in SMEs is becoming more open. This is not
surprising, considering the increasingly important role
small- and medium-sized firms play in innovation. After
all, small firms often lack resources to develop and
commercialize new products in-house and, as a result,
are more often inclined or forced to collaborate with
other organizations.
Drawing on previous work we expected that the
incidence and trend towards open innovation would be
stronger for manufacturing companies and medium-sized
enterprises (as opposed to services companies and small
enterprises, respectively). Manufacturing firms are on
average more active in the outsourcing of R&D and the
out-licensing of IP, a result that is not surprising given the
technological commitment of these firms, but they do not
differ from service firms on other open innovation
activities. This is an important finding; open innovation
is as relevant for service firms as it is for manufacturing
firms, and research about open innovation should not be
limited to those SMEs that have formal R&D activities.
This result is in line with Lichtenthaler’s findings (2008).
He investigated differences between industries in more
detail, and found no significant differences either.
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In contrast, we found significant differences in the
adaption of open innovation practices between different
size classes. Medium-sized enterprises engage in and adopt
open innovation more often than small enterprises. These
firms dispose of the required scale and resources to organize
a broader range of innovation activities, and compared to
small enterprises they may be considered as larger
repositories of knowledge that can be purposively out-
sourced. The survey results furthermore reveal that open
innovation is present in and increasingly adopted by small
enterprises as well, but the adaption rate for all exploration
activities grows faster for medium-sized firms than for small
firms. This result indicates a divergent evolution between
medium-sized firms and their smaller counterparts.
Cluster analysis revealed three groups of SMEs, clustering
firms into groups with similar open innovation practices.
Their features confirm Lichtenthaler’s (2008) conclusion that
companies seldom focus on either technology exploitation or
technology exploration. Rather, open innovating companies
tend to combine these two aspects of open innovation.
Besides, as the cluster of most ‘open’ innovators has
relatively more medium-sized companies, the clustering
implicitly suggests a sequence in the adoption of open
innovation, starting with customer involvement, following
with employee involvement and external networking, and
ending with more ‘advanced’ practices like IP licensing,
R&D outsourcing, venturing and external participations.
The paper also explored motives of SMEs to get engaged
into open innovation and the barriers managers experience
in implementing it in the organization. The results indicate
that open innovation in SMEs is mainly motivated by
market-related targets: SMEs make use of several open
innovation practices at the same time to serve customers
effectively or to open up new markets, with higher-order
objectives to secure revenues and to maintain growth. This
finding corresponds with Gans and Stern (2003), who
argued that the main problem of small enterprises is not so
much invention but commercialization. Cooperation with
industry incumbents might be one way to overcome the
difficulties of commercialization. Knowledge acquisition
and the effectiveness of innovation processes are also
frequently mentioned, usually in the context of technology
exploration practices. Cost and control considerations were
mentioned much less often.
The managerial and organizational barriers to open
innovation are very diverse, but the main barrier to open
innovation in SMEs is related to the organizational and
cultural issues which arise when SMEs start to interact and
collaborate with external partners. These issues are
encountered in a range of innovation activities, including
venturing, customer involvement, external networking,
R&D outsourcing and external participations.
6.2. Limitations
The current study is a first exploration of the open
innovation practices in SMEs. Consequently, it has several
limitations. We identified four major limitations. First, the
measurement of some open innovation practices was very
general as some practices were broadly defined. This
particularly applies to employee involvement, customer
involvement and external networking. These innovation
practices were introduced to respondents in such a way that
most respondents affirmed they were applying these
practices. Although it is uncertain how the definitions
have influenced the outcomes, we probably would get a
more precise view on open innovation in SMEs with more
narrowly defined practices. External networking was for
example defined as ‘drawing on or collaborating with
external network partners to support innovation processes,
for example for external knowledge or human capital’
(Table 2). This practice would include formal strategic
alliances with multiple partners to enable ground-breaking
research, but also relatively simple, informal contacts with
suppliers to develop process innovations. Future attempts
to survey open innovation in broad samples of enterprises
should delineate the several practices in a more detailed
and accurate way.
Next, the list of open innovation indicators is probably
not a complete list. Past studies have proposed other
practices that were not included in the survey. Examples
include the globalization of innovation activities and the
early involved of suppliers in innovation processes (see
Gassmann, 2006). One may argue that globalization of the
innovation process is not relevant for SMEs. Nevertheless,
we suggest that globalization should be included to
complete the picture. As a consequence, we cannot claim
that our survey data capture the full domain of external
technology exploitation and exploration.
Although our sample of SMEs is extensive, there is still a
chance that some types of enterprises were still overlooked.
The screening of respondents implied that start-ups and
micro-enterprises (with less than ten employees) were
excluded. As these enterprises have been repeatedly
identified as sources of breakthrough innovations
and challengers of incumbent innovation actors (e.g.
Schumpeter, 1934), this is an issue that future researchers
should pick up. Moreover, the screening of respondents
based on the presence of innovation activities distorts
the ‘representativeness’ of our sample, i.e. results cannot
be generalized to the population of Dutch enterprises
with 10–499 employees. This is partly due to the screening
questions, but also because it was decided that manu-
facturers had to be over-sampled at the expense of
services. Manufacturers are heavy-users of innovation
policies, and for ‘political’ reasons the commissioner of
the survey had requested detailed covering of this group.
Nevertheless, the sample does reflect a broad group of
innovative SMEs that goes beyond the scope of past open
innovation studies.
Finally, motives and perceived challenges were surveyed
only if respondents reported that they had adopted the
corresponding practices. Due to limited numbers of
respondents our conclusions are only tentative, and for
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outward and inward IP licensing, no results could be
reported. This is regrettable because IP licensing is an
aspect of open innovation that is still in its nascent phase
(Chesbrough et al., 2006) and probably in most need of
detailed investigation (Lichtenthaler, 2007).
6.3. Suggestions for further research
Despite these limitations, the findings of the current
study should encourage scholars to analyze in greater
depth open innovation in SMEs. First and foremost, our
results indicate that open innovation is relevant for much
broader groups of enterprises than just large and multi-
national enterprises or high-tech manufacturing firms, i.e.
the open model is present and increasingly applied
in the whole economy. Future research should broaden
the scope by studying open innovation in broader samples,
also capturing small enterprises and firms in services
Open innovation studies have so far been dominated
by qualitative research approaches, drawing heavily on
in-depth interviews and case studies. Such methods are
welcome to charter relatively new phenomena and to
develop theories (Eisenhardt, 1989), but we anticipate
that in the further research on open innovation, quantita-
tive research methods will and should be applied
more often in order to generalize research outcomes
and to test hypotheses. This is also relevant for policy
makers who will find it hard to justify and develop
policies for open innovation as long as there are no
statistics demonstrating that open innovation is relevant
for large business populations. We consider it a challenge
for statistical offices to adapt current innovation
surveys to better reflect open innovation. In this context,
we remark that current innovation surveys such as the
CIS mainly focus on R&D and innovation investments
of enterprises, and external networking activities, but
do not pay attention to other open innovation practices
(OECD, 2005). Especially technology exploitation activ-
ities are overlooked. The survey presented here
might inspire statistical offices to modify their surveys,
although the above-mentioned limitations should certainly
be accounted for.
The dynamics of open innovation in SMEs is another
research area that should be further developed. Our
findings suggest that some open innovation activities are
easy to implement while others may be picked up later in
the growth cycle of the firm. Cluster analysis revealed three
homogeneous groups of SMEs with similar application of
open innovation practices. The clusters implicitly suggest a
sequence in the adoption of open innovation, starting with
customer involvement, following with employee involve-
ment and external networking, and ending with more
advanced practices which require formal budgets and
greater size, e.g. IP licensing, R&D outsourcing, venturing
and external participations. Future work should further
investigate how organizations engage in open innovation
during these growth phases, and what managerial implica-
tions can be derived.
In addition, the current survey does not study how
large and small firms interact in open innovation.
Christensen et al. (2005) shows that large, established
companies and small start-ups manage open inno-
vation differently, reflecting their differential position
within the innovation system. Hence, future research
should focus on the requirements of open innovation on
differences in culture, structure and decision making
between partners of different sizes and from different
A final recommendation is to study the motives
and challenges related to open innovation in more
detail. We found that market considerations were the
most important reason for SMEs to engage in open
innovation. This suggests that SMEs are motivated to
capitalize on their internal knowledge and to find
alternative pathways to markets. It seems that future
research should pay more attention to the purposive
outflows of knowledge, i.e. technology exploitation activ-
ities. This recommendation is consistent with Lichtentha-
ler’s (2008) observation that the innovation processes of
many enterprises are increasingly marked by external
technology acquisition, but that external technology
exploitation to commercialize technologies is of a more
recent date (p. 148). As for the managerial challenges, we
found that organizational and cultural issues are the
key barriers to implement open innovation. This is well
in line with past interview-based studies (e.g. Chesbrough
and Crowther, 2006) and the current literature on
inter-organizational collaboration in innovation. However
the question remains how SMEs can best deal with this
major barrier.
Annex. Principal component analysis
Table A1 shows component loadings of Open Innova-
tion practices on three components with eigenvalues 41.0
(after varimax rotation). This solution explains 57% of
the variance.
Table A1
Principal component analysis of open innovation practices (n¼605)
Open innovation practice Component 1 Component 2 Component 3
Venturing 0.02 0.08 0.83
Outward IP licensing 0.04 0.82 0.04
Employee involvement 0.72 0.13 0.01
Customer involvement 0.59 0.08 0.10
External networking 0.81 0.07 0.01
External participation 0.11 0.07 0.81
Outsourcing R&D 0.21 0.51 0.13
Inward IP licensing 0.02 0.80 0.06
Variance explained (%) 25 17 15
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