Pain shared, pain halved? Cooperation as a coping strategy for innovation barriers
Pain shared, pain halved? Cooperation as a coping strategy for innovation barriers
Davide Antonioli 0 1
Alberto Marzucchi 0 1
Maria Savona 0 1
JEL Classification 0 1
0 SPRU, University of Sussex , Jubilee Building, Falmer, Brighton BN1 9SL , UK
1 Department of Management and Business Administration - DEA, University of Chieti-Pescara , Chieti , Italy
The paper analyses the relationship between the perception of barriers to innovation and the firm's propensity to cooperate to mitigate their effect. First, we look at whether cooperation with research organizations or private firms is associated with experiencing different types of barriers, for example, financial constraints, lack of human capital or uncertain market demand. Second, we test whether experiencing several types of barriers simultaneously has a super-modular effect on the propensity to cooperate tout court, and the choice of cooperation partner. We find that having to face a single, specific constraint leads to firms 'sharing the pain' with cooperation partners-both research organization and other firms. However, the results of a super-modularity test show that having to cope with different barriers is a deterrent to establishing cooperation agreements, especially when firms lack finance, adequate skills and information on technology or markets. The paper adds to the innovation literature by identifying the factors associated with firms' coping with different barriers by applying a selective cooperation strategy.
Barriers to innovation; Innovation cooperation; Firm behaviour; Innovation policy
1 Introduction and Background
Since the late 1990s, a flourishing literature on cooperation for innovation has focused on
different forms of collaborations among actors. These collaboration types range from
research joint ventures, non-equity contractual collaborations and joint projects, to formal
and informal arrangements (Hagedoorn et al. 2000; Gallie´ and Roux 2010), both among
private firms and between private firms and public research organizations. In terms of the
object of cooperation, the majority of this literature looks at cooperation within the realm
of R&D projects, which usually are considered more costly and more risky (Kleinknecht
and Reijnen 1992; David and Hall 2000; Fritsch and Lukas 2001; Miotti and Sachwald
2003; Lo´ pez 2008).
Analyses of the mechanisms that govern cooperation for innovation are based on
different theoretical perspectives. The industrial organization literature (e.g. Kaiser 2002)
suggests that incentives to cooperate are associated with strategies to internalize
knowledge that can leak out to competitors (Arvanitis 2012). The management literature shows
that firms decide to cooperate if they can foresee opportunities to shape the competitive
environment, upgrade capabilities, gain access to new resources and learn to use
new/complex technologies (Caloghirou et al. 2003). Relatedly, the large scholarship on
absorptive capacity following the seminal work by Cohen and Levinthal (1989, 1990),
identifies mechanisms that enable firms to exploit externally acquired knowledge (e.g.
Zahra and George 2002; Franco et al. 2014). Overall, as suggested by de Faria et al. (2010,
p. 1083), the decision to cooperate is based on how firms ‘‘manage the trade-off between
generating and receiving knowledge spillovers to and from partners’’, and depends on the
type of partner, the absorptive capacity of the cooperating firm and the type of innovation
involved (de Faria et al. 2010).
The above theoretical perspectives tend (with a few exceptions) more often to associate
incentives to cooperate with potential gains in terms of learning and knowledge spillovers
rather than the potential opportunities to minimize a loss or cope with a problem.1
However, none of these literature streams considers experiencing barriers to innovation as
an incentive to cooperate.
Within the recent and burgeoning literature on the barriers to innovation, several
contributions look at: (i) firm characteristics affecting the perception of financial and
nonfinancial obstacles to innovation (D’Este et al. 2012, 2014; Ho¨ lzl and Janger 2013, 2014;
Pellegrino and Savona 2013); (ii) the deterrent effect of obstacles on firms’ decisions to
engage and invest in innovation activity, and the propensity to innovate (Mohnen and Rosa
2002; Baldwin and Lin 2002; Galia and Legros 2004; Canepa and Stoneman 2008; Tiwari
et al. 2008; Savignac 2008; Iammarino et al. 2009; Mancusi and Vezzulli 2010); and (iii)
the direct and indirect impact of barriers on firms’ productivity and performance more
generally (Coad et al. 2016) (see Hall et al. 2016, for a recent review on the subject).
The extant literature on the barriers to innovation has missed the opportunity to look at
whether and how firms change their strategy in response to their perception of obstacles.
Also, while contributions on barriers have focused on the direct effect of barriers on firms’
innovation engagement and performance, they have not explored the indirect effect that
1 For instance, from an industrial organization perspective, Lo´pez (2008) finds that a cost-risk sharing
strategy is the main determinant of cooperation in R&D projects in the case of Spanish firms. Barge-Gil
(2010, p. 198) posits that ‘‘Cooperation for innovation is more important for firms required to overcome
more obstacles in its pursuit’’.
barriers might induce on performance through a change in the firm’s strategy, for example,
increasing their cooperation with different partners.2
This paper aims to contribute to the cooperation and the barriers to innovation literature
by addressing the gap identified above. We look at whether cooperation might be a viable
strategy to cope with financial and non-financial (knowledge or market related) barriers3
and whether the choice of cooperation partner(s) is associated with experience of a
particular barrier or, indeed, the simultaneous presence of several different barriers.
Specifically, the first aim is to ascertain whether, ceteris paribus, firms that experience
financial and non-financial obstacles to innovation engage more in cooperation, and ‘share
the pain’ of specific bottlenecks in the attempt to innovate. Cooperation can be an
important strategy for coping with cost-related barriers. The literature shows that
collaboration with external partners can produce cost- and risk-sharing opportunities (Hagedoorn
et al. 2000; L o´pez 2008). These reduce internal financial constraints and, through the
pooling of risk, the cost of external funding. Cost-related constraints can be further
attenuated through economies of scale and scope, which are likely to arise from
collaboration with external partners (Becker and Dietz 2004; Arvanitis 2012). The decision to
engage in cooperation can be dictated also by the need to handle internal knowledge and
skills shortages. Partnering with other firms or research organizations can grant access to
crucial knowledge that is not available within the focal firm (Barge-Gil 2010), and can lead
to upgrading of competences and skills (Caloghirou et al. 2003). Furthermore, firms can
benefit from knowledge sharing and complementarities, which allow for the use of parallel
and wider knowledge packages, whose development and maintenance might be too
difficult for firms innovating in isolation (Ahuja 2000). Finally, cooperation can represent a
viable strategy for coping with market related barriers. Collaboration for innovation might
raise appropriability issues, which could erode the firm’s competitive advantage (Cassiman
and Veugelers 2002; Veugelers and Cassiman 2005). However, cooperation can also
increase the firm’s capacity to enter new markets, reposition and expand in existing ones,
capture technological information to face changes in demand and bring technologies to the
marketplace rapidly (Mowery et al. 1998; Hagedoorn et al. 2000; Wu 2012). Given the
above, we address the following research question:
Do barriers to innovation lead to cooperation?
With respect to Q1, our conjecture is that facing cost, knowledge and market related
barriers increases the proclivity to resort to cooperation as a coping strategy, particularly to
share the costs and risks of innovative activities and to outsource to compensate for assets
that are lacking when engaging in innovation.
Our second objective is to ascertain whether experiencing a specific type of barrier
(related, e.g., to cost, knowledge or market) leads, ceteris paribus, to engagement with a
specific type of partner (e.g. customers, research organizations). As mentioned above, the
cooperation literature has been looking at cooperation beyond the realm of R&D activities,
based on engagement with a variety of partners (Belderbos et al. 2004, 2006). This broader
2 A notable exception is D’Este et al. (2014), which looks at how firms reduce the barriers to innovation by
increasing their training expenditures.
3 Section 2 and Table 1 describe at greater length the types of financial, knowledge and market obstacles
included in European innovation survey questionnaires and used in this work. In particular we refer to cost
barriers (lack of internal funds, lack of external funds, or excessive cost related to innovation); demand and
market structure barriers (demand uncertainty or a market structure dominated by incumbent large firms);
knowledge barriers (lack of skilled personnel, lack of information on markets, or lack of information on
view of cooperation identifies firm characteristics that influence the different propensity of
firms to cooperate with ‘specialized knowledge suppliers’ (Tether 2002; Schmidt 2005;
Tether and Tajar 2008a, b). The spatial, social and cognitive proximity with external
partners, alongside the degree of engagement in innovative activities, either R&D or
nonR&D related (D’Este et al. 2013), affect not only the propensity to cooperate but also the
choice of partners. Evidence suggests that choice of partner or partners depends on two
different strategies: (i) firms seeking to benefit from horizontal spillovers and aiming to
define technological trajectories that are new to the firm or the market, tend to choose
universities, or public or private research organizations; (ii) firms that want to build
incrementally on their existing knowledge and to benefit from vertical spillovers will
instead choose customers and suppliers as cooperation partners (Barge-Gil 2010; De Faria
et al. 2010). On the above premises, we address the following research question:
Do different barriers lead to collaboration with specific cooperation partners?
Our conjecture on Q2 is that the choice of partner is dictated by the coping strategy for
the specific problem(s) encountered. While we expect that finance barriers can be
alleviated by sharing costs with any type of partner, knowledge and skills shortages may lead
firms to prioritize cooperation with research organizations. Market uncertainty might lead
to cooperation with customers and suppliers rather than competitors or research
Our third objective is to investigate whether the perception of several different barriers
simultaneously might complement (super-modular effect) or substitute (sub-modular
effect) the decision to cooperate. Indeed, it is likely that firms will experience the joint
presence of more than one barrier. Scholars have investigated cooperation failures
(Lhuillery and Pfister 2009) and the obstacles to cooperation (Mora Valent´ın 2000), which
hint at the possible bottlenecks to intensifying cooperation as a response to multiple
barriers. A particularly novel aspect of our contribution is the specific focus on the
presence of a super- or sub-modular effect of barriers on cooperation, which is an adaptation to
the methodological framework proposed by Mohnen and R o¨ller (2005), who examine
complementarity in innovation policy instruments using evidence on obstacles. This leads
to our third research question:
Q3 Are the perceived barriers complements or substitutes as determinants of cooperation
Due to the scant prior related work, we do not formulate explicit expectations about Q3.
We rely on our empirical application to shed light on the complementarity/substitutabiliy
effect of barriers on the propensity to cooperate.4
We address Q1–Q3 empirically by drawing on the fourth wave of the French
Community Innovation Survey 2002–2004 (French CIS4). Results show that experiencing
barriers is associated with the adoption of cooperation strategies. Financial barriers are
positively related to all types of cooperation. Thus, firms resort to cooperation, first and
foremost, as part of a risk and cost-sharing strategy. It emerges also that knowledge
obstacles trigger cooperation with research organizations: as expected, firms collaborate
with research institutes and universities to mitigate shortages of skills and competencies. In
addition, results provide robust support for the absence of super-modularity and the
4 Whether cooperation exacerbates innovation barriers or reduces them is an aspect that represents a future
extension of our research. Here, the analysis is limited to the effect of one or more barriers on the
presence of substitutability effects from the perception of several barriers simultaneously.
While a firm experiencing a specific obstacle is prone to ‘‘share the pain’’ with partners, the
experience of several barriers jointly does not exert a cumulative effect; there is no
evidence of cooperation intensification. On the contrary, the joint presence of knowledge and
financial obstacles, in particular, reduces the propensity to cooperate. A spectrum of
innovation obstacles, which includes knowledge shortages and, thus, possibly involves low
absorptive capacity, might lead firms to refocus on internal activities, thereby reducing
The questions addressed in this paper and our findings are highly relevant for policy.
From the perspective of extant theoretical framework(s) for innovation policy and as
pointed out by Coad et al. (2016), we do not consider policies to reduce the barriers to
innovation as typical cases of fixing market failures (Arrow 1962). Rather, our analysis
contributes to a better understanding of policies aimed at dealing with system failures (e.g.,
Woolthuis et al. 2005; Metcalfe 2005). Indeed, the case for mitigating interaction failures
among innovation system organizations—firms and research organizations—is reinforced
by evidence on whether cooperation allows firms to cope with innovation barriers.
The remainder of the paper is organized as follows. In Sect. 2 we describe the data and
the empirical strategy. In Sect. 3 we present and discuss our results. Section 4 concludes.
2 Data and empirical strategy
We focus on the case of France. Robin and Schubert (2013) provide an interesting
comparative narrative on the French context in terms of public research institutes. France,
historically, has been characterized by a centralized and mission-oriented science and
technology policy, where missions are defined at the central level and implemented by
national, publicly funded research centres such as CNRS, INRA, INRIA and INSERM.
Interestingly, for the purposes of our work, until 2006 (under the Act of Law of 12 July,
1999) the National Research Agency (ANR) did not provide explicit public financial
support for collaborative research projects involving public research organisations and
private firms (Robin and Schubert 2013). Because our empirical investigation is based on
data from the French CIS4,5 our empirical findings unravel the tendency for firms to
cooperate for innovation before and regardless of the introduction of the 2006 tax credit
The CIS is conducted in EU countries, using a EUROSTAT harmonized questionnaire
based on the OECD Oslo Manual (OECD 2005). The French CIS4 was launched in 2005. It
targeted a representative sample of firms with more than 10 employees, in non-agricultural
sectors; 25,000 firms were interviewed, a response rate of around 86%.6 The majority of
French CIS4 variables cover the 3-year period 2002–2004; some information on firm
structure (e.g., employment, turnover) refer to the initial and final years of the period, and
innovation expenditure and outcome variables refer to 2004.
5 In this respect, note also that the French CIS2008 (focusing on years 2006–2008) did not include
information on barriers, the French CIS2010 (focusing on years 2008–2010) concentrated on a period
largely affected by the global economic crisis, the French CIS2012 (2010–2012) did not include information
on barriers and focused on a period partially affected by the crisis, the French CIS2014 (2012–2014) was not
available at the time of the writing.
6 See http://www.insee.fr/sessi/enquetes/innov/cis4/cis4.htm and http://www.insee.fr/sessi/4pages/222/
principal.htm (last accessed: May 2016) for more information.
Although we rely only on cross-sectional data, the French CIS4 questionnaire includes a
wealth of information covering firms’ structure and location, innovation inputs, outputs and
outcomes, and—most importantly for our analysis—innovation barriers and cooperation
with external partners. The last two items are important because they allow us to retrieve
information on both cooperation and barriers in a period (i.e. 2002–2004) when perception
of obstacles was less likely to be affected by the (probably unobservable) confounding and,
thus, biasing, factors related to the last global economic crisis.
In what follows, we focus on manufacturing firms only. Given the filtered structure of
the CIS questionnaire and in order to have complete information for all the firms included
in our sample, we restrict our sample to innovative firms with no missing values for the
variables employed (see Mairesse and Mohnen (2010) for a discussion on the opportunities
and constraints from the CIS filtering).7 Our working sample includes 3825 firms.
We exploit a twofold econometric strategy. First, we investigate whether and how
specific barriers linked to cost, market and knowledge factors are related to specific types
of cooperation. To this end, we estimate a series of probit models as follows:
Cooperationi ¼ a þ b1Xi þ b2Barriersi þ ei:
where Cooperation denotes the dependent variable the firm’s cooperation agreements with
different types of partners; X is a vector of appropriate control variables; Barriers is a
vector of the variables synthesizing specific types of obstacles to innovation perceived by
the firm; and e is the error term. A further, augmented specification includes the interaction
terms between pairs of barriers in order to obtain a preliminary sense of the potential
influence of the joint perception of barriers on the propensity to cooperate.
Our dependent variables refer to the firm’s engagement in innovation cooperation. We
employ a general binary variable, COOP, which captures whether the firm is engaged in
formal cooperation with any type of partner. We distinguish between cooperation with
firms (COOPFIRM) and research organizations (COOPORG). As mentioned in Sect. 1, the
choice of partner might be dictated by different incentives, such as cost and risk sharing or
outsourcing of information on technologies.
Key explanatory variables include a set of dummies that indicate perception of obstacles
to innovation. These binary variables are built on the 4-point likert scale items included in
the CIS questionnaire, and take the value 1 if the firm reports high relevance for the
influence of at least one item related to: costs (COST) (lack of internal funds, lack of
external funds, or excessive cost related to innovation); demand and market structure
(MKT) (demand uncertainty or market structure dominated by incumbent large firms);
knowledge (KNOW) (lack of skilled personnel, lack of information on markets, or lack of
information on technologies). It is important to remember that the barrier variables
synthesize perception of their importance for innovation-active firms (i.e., firms that have
engaged in innovation): in this respect, according to the classification proposed in D’Este
et al. (2012) and used in other studies, we focus on the revealed rather than the deterring
barriers, which are the hampering factors encountered in the production of innovations,
rather than obstacles that deter firms from engaging in innovation activities (D’Este et al.
2008, 2012; Pellegrino and Savona 2013; Ho¨ lzl and Janger 2013, 2014).
7 The structure of the CIS in most European countries includes a filter on questions related to the innovative
behaviour of firms: while questions on the barriers to innovation are addressed to both innovative and
noninnovative firms (e.g., those declaring they had (had not) introduced a product or process innovation),
questions on cooperation are addressed only to firms stating they introduced a product or process innovation
or engaged in innovation activities.
Table 1 List of variables
Engagement in innovation cooperation agreements (D)
Cooperation with other firms (Other firms in the same group, Suppliers, Customers,
Cooperation with research organisations (Private R&D institutes and labs, Universities,
Public research organisations) (D)
Highly relevant barriers related to: lack of internal funds OR lack of external funds OR
excessive cost related to innovation (D)
Highly relevant barriers related to: lack of skilled personnel OR lack of information on
markets OR lack of information on technologies (D)
Highly relevant barriers related to: demand uncertainty or market structure dominated by
Log-transformed number of employees in 2002
Firm belonging to an industrial group (D)
Affiliate to a foreign group (D)
Receipt of public funding to innovation (regional, national or EU level) (D)
Engagement in continuous R&D (D)
Export to foreign markets (D)
Variables are defined over the reference period 2002–2004, unless differently specified; (D): dummy
Building on these variables, we create a set of interactions to provide a preliminary
picture of whether and how different types of barriers are complements influencing the
cooperation propensity. To this end, we constructed the interactive variables COST*MKT,
COST*KNOW and MKT*KNOW, which we add to the baseline specification to address
the first two research questions.
The control variables in X aim to reduce potential omitted variable bias. First, we
control for firm size, measured as the logarithm of employment. Size can be related to
cooperation since large firms are more likely to adopt a combined strategy of internal and
external knowledge acquisition (Veugelers and Cassiman 1999) due to their critical mass,
resources and likely higher capacity to manage cooperation agreements effectively
(Belderbos et al. 2004; Segarra-Blasco and Arauzo-Carod 2008).
We control also for technological capability, by including a dummy variable (i.e. R&D
Continuous), which captures continuous engagement in R&D investment (e.g., presence of
a dedicated department). This may exert a positive effect on the propensity to cooperate
(e.g. Colombo and Garrone 1996; Cassiman and Veugelers 2002; Belderbos et al. 2004).
Indeed, persistent and sustained engagement in R&D reflects higher absorptive capacity
(Cohen and Levinthal 1989), higher capacity to take advantage from cooperation and thus
to recognize its strategic value.
We control for the effect of public funding (R&D Funding) on the probability to
cooperate since innovation policy programmes may explicitly require firms to cooperate
(e.g. in the case of collaborative R&D subsidies). Public support can change the strategic
behaviours of beneficiaries and how they conduct their R&D and, eventually, might lead to
increased cooperation with external partners (Marzucchi et al. 2015).
Two dummies for whether firms belong to a national group (GROUP) or to a
transnational corporation (TNC) are included. Firms belonging to a group, either national
Table 2 Descriptive statistics
or international, are expected to be more likely to cooperate. When looking for partners,
they benefit from the power and prestige of the wider group. In addition, firms belonging to
foreign groups may need to establish cooperation agreements in order to acquire specific
local knowledge and capabilities, for instance, related to local markets requirements (e.g.
Tether 2002). Moreover, they may exploit intra-group communication channels and
knowledge pools to gather more information about potential partners, create easier contacts
and more easily tap into knowledge from the interacting firms or organizations (Tether
2002; Mohnen and Hoareau 2003).
We include firms’ engagement in international markets (EXPORT) which might exert
an effect on cooperation. On the one hand, exporting firms may revert to cooperation to
acquire capabilities and maintain their competitiveness. On the other hand, when faced
with a strong competitive environment, exporting firms may be induced to protect their
know-how (Cassiman and Veugelers 2002) and may reduce cooperation in order to
minimize knowledge leakages.
Finally, we include two sets of dummies. The first is the firm’s regional location (NUTS
2).8 It accounts for regional heterogeneity in terms of availability of cooperating partners
and structural, institutional and social aspects, which might affect the propensity for
cooperation (for an interesting take on this issue, see D’Este et al. 2013). Institutional
features can provide different grounds for cooperation activities because of the different
formal and informal instruments at the regional/local level to stimulate cooperative
activities between firms and other institutional actors such as universities and research
centres (Robin and Schubert 2013). Also, some regions are more industrialized than others,
providing firms with a large ‘reservoir’ of partners to choose for cooperation activities.
Moreover, the cognitive distance among cooperating partners may be shorter if partners are
located within the same regional borders, making it easier for the same partners to
cooperate. Overall, each region has idiosyncratic specificities that can influence the
propensity of embedded firms to cooperate. The second is a set of NACE 2-digit dummies
to control for sector specificities.
8 We exclude from our sample firms located in the French overseas territories since the availability of
suitable cooperating partners in the proximity might be very limited for these firms.
) 1 .50 .00 .00 .00 .20 .00 .10 .10 .20 .10
COOPj ¼ COOPjðb0; b00; hjÞ8j:
Each firm j faces a combination of the two barriers, ðb0; b00Þ and a set of endogenous and
exogenous controls hj, including the remaining barrier.
Complementarity between the two different barriers can be analysed by testing whether
COOPjðb0; b00; hjÞ is super-modular in b0 and b00. Our aim is to derive a set of inequalities to
be tested in the empirical analysis.
Each firm might be in one of the four following states of the world: facing both b’ and
b’’; neither of the two; or one, but not the other one; and vice versa. This leads to four
consequent elements in the set B (forming a lattice):
B ¼ ff00g; f01g; f10g; f11gg:
COOPjð11; hjÞ þ COOPjð00; hjÞ
COOPjð10; hjÞ þ COOPjð01; hjÞ;
COOPjð11; hjÞ COOPjð00; hjÞ COOPjð10; hjÞ
þ COOPjð01; hjÞ COOPjð00; hjÞ
This second inequality clearly shows the interpretation of the super-modularity, or
complementarity between two barriers. If the inequality holds, it means that the gain in the
propensity to cooperate (increase in the probability to cooperate) that the firm achieves by
moving from a state of the world characterized by the absence of relevant barriers (0,0) to a
state of the world in which both barriers are perceived as relevant (1,1), is higher than the
sum of the gains in the propensity to cooperate (increases in the probability to cooperate)
obtained by moving from a state of the world (0,0) to those in which only one barrier is
perceived as relevant (1,0) and (0,1).
9 We confine our analysis to the inclusion of two pairs of barriers at a time, given the complexity in
interpreting the implications highlighted for policy. To the best of our knowledge, a complementarity test,
based on inequality restrictions, on three or more variables (e.g. triplets, quadruplets), has never been
implemented. In addition, and again to the best of our knowledge, in STATA (the statistical software we
used for the analysis) there is no routine that allows joint testing of several inequality restrictions, on pairs of
variables (i.e. not triplets or quadruplets), as Mohnen and Ro¨ller (2005) do using a routine in GAUSS.
In order to test for complementarities or substitution effects we operationalize the
methodological framework in two steps.
In the first step we set up the ‘Cooperation function’, which can be specified as follows,
using two types of barriers, e.g. COST and MKT,10 to define the states of the world, while
controlling for both the third barrier (e.g., KNOW) and the set of control variables defined
½COOP i ¼ b0i½Controls þ aKNOW
þ b4i½COSTð0Þ=MKTð0Þ þ ui
Both the cooperation variable (COOP) and the two types of cooperation (COOPORG
and COOPFIRM) are dummy variables: therefore, we run a set of probit regressions,
excluding the constant term, since we are interested in the marginal effects associated with
all the four states of the world b1, b2, b3 and b4. It is important to stress that while we focus
on the complementarity between two types of barriers (e.g. COST and MKT), we control
for a third type of obstacle (e.g. KNOW). Specifically, the marginal effects associated with
the four states of the world used in the complementarity test are computed setting at 0, 1,
the mean value and excluding the third barrier. This allows us to infer whether the results
of the complementarity between two barriers typologies test hold for the different values of
the third type of obstacle.
Having retrieved the marginal effects using the probit estimates, the next step is to
implement a set of Wald tests, which allow us to test the following linear restriction on the
state-of-the-world-dummies’ marginal effects: b1 ? b4 = b2 ? b3 where b1 is associated
with the (1,1) state of the world; b2 is associated with the (1,0) state of the world; b3 is
associated with the (0,1) state of the world and b4 is associated with the (0,0) state of the
The Wald tests are distributed as a v2 with one degree of freedom since we are testing a
single linear restriction at a time. Given that we are interested in the following inequalities,
b1 ? b4 - b2 - b3 C 0; b1 ? b4 - b2 - b3 B 0, and since each Wald test has one degree
of freedom, we can apply the appropriate procedure for the p value adjustment in testing
inequalities.11 Moreover, as a further robustness check, we combine the results of the
‘adjusted’ Wald test with the resulting sign of the linear combination of the coefficients.
By looking at the joint sets of results, we can infer whether rejection of the Wald test
null hypothesis allows us to identify complementarity or substitutability between barriers:
on the one hand, if b1 ? b4 - b2 - b3 C 0 and the Wald test leads us to reject the null, we
can argue that we are in presence of super-modularity and, hence, of complementary
barriers; on the other hand, we infer sub-modularity if b1 ? b4 - b2 - b3 B 0 and the
Wald test null is also rejected.
10 The same reasoning holds for other couples of barriers.
11 See http://www.stata.com/support/faqs/statistics/one-sided-tests-for-coefficients/.
3.1 Baseline probit
The results of our baseline probit are reported in Table 4, with the marginal effects
reported in Table 5. We test six specifications of the baseline probit. The models test the
effect of our main regressors and control variables respectively on:
The probability of engaging in cooperation in general (models 1 and 4), which
responds to our first research question (Q1) about whether experiencing barriers of any
type is associated with the propensity to cooperate;
The probability to cooperate with other firms (models 2 and 5);
The probability to cooperate with research organizations (models 3 and 6). The last
two points respond to our second research question (Q2).
The first triplet of models (1–3) includes proxies for single barriers only; the second
triplet (models 4–6) also includes the interaction terms. More specifically, as mentioned
above, we check whether experiencing financial constraints, lack of knowledge or market
structure barriers affects the probability of cooperating in general and with specific partners
(models 1–3); we test also whether facing joint obstacles (in pairs) increases the chances of
cooperating (in general, and with specific partners) or, rather, deters firms from cooperating
as much as they might had they experienced only a single barrier. We reprise this latter
issue within a complementarity test, which allows us to investigate further whether a
positive (negative) effect, emerging from the interactive dummies, translates into an
augmenting/super-modular (diminishing/sub-modular) effect of joint obstacles on the
propensity to cooperate in general, and with specific partners (which responds to our third
research question Q3).
Indeed, in our discrete setting, the tests for complementarity are more informative than
the probit results. In our case, it is not possible to impose continuity on the variables
capturing the barrier perception and investigating complementarities through the analysis
of mixed partial derivatives (Milgrom and Roberts 1995). However, we are able to test
whether perceiving one barrier increases more (or less) the propensity to cooperate, in case
the perceived relevance of the other barrier increases. The complementarity test might, in
turn, add more fine-grained insights. For instance, as we will see in the following—in the
case of knowledge and market barriers for cooperation, in general and with other firms—
while the interaction terms point to non-significant effects, the complementarity test may
reveal the presence of sub-modularity. In this case, our test points to a situation in which
perceiving both barriers increases the probability of cooperation less than if the firm
perceives single barriers.
Turning to the presentation of the results, the first piece of evidence is that experiencing
financial barriers is a robust, significant driver of cooperation, both in general and across
different partners. In line with the literature (de Faria et al. 2010; Barge-Gil 2010, among
others), carrying out expensive innovation is a major driver of cooperation, which emerges
as a cost-sharing strategy. Interestingly, the perception of relevant financial constraints
seems to be related more robustly to a higher tendency to cooperate with research
In relation to cooperation with research organizations, one of our conjectures is
confirmed: experiencing knowledge barriers linked to information on technologies and lack of
qualified human capital, is associated with a positive and significant coefficient of
)(5 IPFCROOM .14096*** .()00581 .20434*** .()00813 .00991 .()00732 .02957***- .()01048 .00288 .()00991 .01563- .()01078 .01373*** .()00187 .02754*** .()00571 .*03555** .()00684 .03082*** .()00459 .02968*** .()00473 .00889 .()00612 SEY SEY .12346*- .()04153
Table 5 Baseline probit estimations (marginal effects)
Robust standard errors in parentheses, constant, regional and sector dummies included
* p \ 0.10; ** p \ 0.05; *** p \ 0.01
cooperation with research organizations. This is in line with much of the literature on
cooperation: universities and public and private research organizations can be providers of
specialized knowledge for firms (Tether and Tajar 2008a), especially if the firm lacks the
internal capacity to fill its knowledge gaps.
This finding is strengthened by the second triplet of specifications, which include the
interaction dummies and account for likely interdependencies among the different barriers.
Models 4–6 show that knowledge barriers are as important as financial barriers for driving
An interesting result is that obstacles related to market structure and stagnating demand
seem not to matter for cooperation strategies. Facing an incumbent dominated market is
not associated with coping by establishing cooperation agreements. The lack of
significance of the coefficient of market barriers seems to dominate most of the interaction
dummies, confirming that market structure is not a strong driver of cooperation. It is
interesting that the literature on barriers shows that, in general, lack of or stagnant demand
are relevant factors affecting innovation investments (Pellegrino and Savona 2013;
GarciaQuevedo et al. 2014); however, at least in the French case, it seems that neither is
associated with cooperation behaviour. It might be—although we are not able to test this
here—that market barriers are mainly deterrent rather than revealed, so that firms
experiencing lack of demand or a competitive market structure do not initially engage in
One of the most consistent results of models 4–6 is that when firms face both cost and
knowledge barriers, the propensity to engage in cooperation decreases. This suggests that
among firms faced with the need to cope with different types of obstacles, cooperation is
not the preferred strategy since it could imply a trade-off in terms of the resources needed
to manage the cooperation. The benefits of cooperation rely on a certain (benchmark?)
level of absorptive capacity, especially cooperation with research organizations, and the
transaction costs linked to gathering information, selecting partners and establishing the
cooperation. All of these aspects might represent a trade-off in terms of resources, which
might deter firms from engaging in cooperation. In other words, there seems to be a sort of
‘‘cap’’ on the convenience of cooperating as a coping strategy if the problems to be dealt
with are too many or are too diverse. When we look at the interaction term of knowledge
and market barriers, it seems that firms operating in difficult markets are discouraged from
cooperating with research organizations. Therefore, it seems that outsourcing knowledge to
private or public research organisations is more ‘‘costly’’ if the firm is operating in an
overly competitive or a stagnant market.
We extend our discussion of these results in the context of the complementarity test
3.2 Complementarity test
The complementarity test qualifies the results emerging from the baseline probit, especially
models 4–6, which include the interaction terms, by uncovering their complementary or
substitution effect. We gathered a great deal of information on whether firms facing
combinations of several barriers (financial and lack of knowledge; financial and market
structure; lack of knowledge and market structure) tend to revert to cooperation more, and
tend to choose a particular set of partners.
In general, we expect the negative (positive) signs of the interaction dummies to be
confirmed by a sub-modularity (super-modularity) effect when the Wald tests are applied.
In addition, as mentioned above, the tests help to highlight results that were not statistically
significant in the baseline probit estimations. A negative (positive) although not significant
interaction, might be hiding the presence of substitutability (complementarity) between
two barriers. The ‘global’ effect captured by the interaction term does not reveal the
presence or not of complementarity or substitutability effects.
The results of the complementarity tests are reported in Table 6. The tests show no
evidence of complementarity (super-modularity). Instead, we find evidence of
substitutability (sub-modularity) between two couples of barriers—Cost/Knowledge and Market/
Knowledge—which confirms and qualifies the results of the baseline probit. The
substitutability is not sensitive to the control for the third barrier, which is alternately set to 1, 0,
and to its average, in the calculation of the marginal effects on which the tests are
conducted. As a further robustness check, we excluded the third barrier from each
specification, but the results do not change.
The presence of substitutability in the pairs Cost/Knowledge and Market/Knowledge,
strictly speaking, means that the increases in the propensity to cooperate, as a result of
perceiving one or the other barrier in each pair as relevant, are larger than the increase in
the propensity to cooperate induced by a switch from the case of absence of relevant
barriers to the case in which both of the barriers in each pair are perceived as relevant. In
.140 .()035 .5151* .()000 .18*7* .()000 .140 .()035 .5491* .()000 .272** .()000 .104 .()035 .5145* .()000 .721** .()000 .010 .()037
* * *
.205 .()031 .974** .()000 .387** .()002 .025 .()031 .97*5* .()000 .38*7* .()002 .025 .()013 .975** .()000 .387** .()002 .022 .()032
W W W
su T O T su T O T su T O T su T
s K N K s K N K s K N K s K
r r r r
e M K M e M K M e M K M e M
v v v v
irrreab ST ST OW irrreab ST ST OW irrreab ST ST OW irrreab ST
ts O O N ts O O N ts O O N ts O
1 C C K 1 C C K 1 C C K 1 C
other words, the convenience for firms to cooperate when perceiving cost/knowledge or
market/knowledge barriers as jointly relevant, seems to be ‘‘sub-optimal’’. Thus, firms
might resort to strategies other than cooperation to overcome the obstacles to innovation if
these barriers are perceived as jointly relevant.
3.3 Summary of results
Overall, the probability to cooperate is influenced by the perception of relevant barriers.
Indeed, the propensity to cooperate emerges as positively related to the presence of single
barriers, which ‘induces’ cooperation more than does the joint, complementary presence of
three pairs of barriers. This emerges from both the probit estimates and the
complementarity tests which focus on pairs of barriers (still controlling for the third barrier not
included in the test).
When we focus on patterns of cooperation with research organizations, the results point
to a tendency to engage in cooperation with knowledge providers, mostly if the main
problem is lack of appropriate information and human capital. Whenever this barrier is
perceived in conjunction with others, the effect is sub-modular, that is, other issues become
involved and the firm tends either to diminish its propensity to revert to cooperation with a
research organization or to not cooperate at all.
The effects of the control variables on cooperation generally are in line with the
literature. Receiving public funds to carry out R&D, persistence in R&D investments,
exporting to foreign markets, belonging to a group, being a TNC’s affiliate and size are all
associated with higher levels of cooperation. These results are largely robust across all
4 Concluding remarks
This paper contributes to the innovation literature by questioning and empirically testing
whether experiencing barriers to innovation is associated with engagement by firms in
cooperation for innovation in general, and with specific partners, as a coping strategy. In
doing so, the paper bridges two streams of literature which so far have been separate: an
established strand of work on innovation cooperation and a more recent stream on the
barriers to innovation.
We briefly reviewed the relevant contributions in both areas. Innovation cooperation has
been studied from several theoretical perspectives, from industrial organization to
management, including scholarship on absorptive capacity. All generally identify firms’
incentives to cooperate, ranging from knowledge internalization (Arvanitis 2012), learning
in complex environments (Caloghirou et al. 2003) and gaining benefit from absorptive
capacity (Franco et al. 2014), to risk and cost-sharing strategies (de Faria et al. (2010). A
large number of contributions within these theoretical perspectives focus on cooperation in
R&D projects and touch on the issue of appropriability. This is particularly important for
explaining the determinants of and constraints to cooperation with different actors, both
research organizations and private firms, along the value chain, and on activities not
necessarily limited to R&D projects (de Faria et al. 2010; Barge-Gil 2010).
We have argued that the large number of contributions on the determinants of (R&D
and non-R&D related) innovation cooperation rarely consider the minimization of losses,
reducing the risk of failure and coping with the obstacles to innovation, as incentives to
Firms might need to outsource knowledge or share costs and risks linked not only to
basic research but also to the implementation and launch of an innovation, for instance, in a
market dominated by an incumbent or where the adoption and diffusion of innovation is
uncertain. A paper by Laursen and Salter (2014) analyses empirically the ‘‘paradox of
openness’’, that is, how firms manage the opposing incentives of the search for external
knowledge and maximization of appropriability. They find a concave relationship between
the breadth of external search and formal collaboration for innovation, and the strength of
the appropriability strategy. Cooperation might be costly, impose a too-high trade-off in
terms of the resources needed to manage it or of appropriability, and it can be subject to
failure (Lhuillery and Pfister 2009).
In this context, this paper addressed the important issue of disentangling whether
cooperation activities with different partners are the result of a coping strategy to which the
firm resorts when faced with one or more barriers to innovation, or a particular
combination of these barriers.
It is important to bear in mind that what we infer from our empirical analysis is based on
statistical association rather than on causal effects. Our analysis is a cross-sectoral
estimation of the probability to engage in cooperation as a function of perception of one or
three combinations of pairs of obstacles, and several control variables. We have provided a
complementarity test of the super- or sub-modular effect on cooperation of a status shift
from absence of barriers to perception of one or two barriers to innovation in several
combinations (cost/knowledge; cost/market; knowledge/market) of barriers.
Results show that facing barriers generally is associated with the adoption of
cooperation strategies, and particularly in the case of financial barriers, which are shown to be
positively related to all types of cooperation. Firms resort to cooperation based on a
costsharing strategy. Knowledge obstacles trigger cooperation with research organizations:
firms collaborate with research organizations to mitigate shortages of skills and
Our results confirm also that cooperation is subject to a sort of diminishing returns effect
if used as a coping strategy to mitigate the obstacles to innovation: our results provide
robust support for the absence of super-modularity and the presence of substitutability
effects of the joint perception of barriers. While a firm experiencing a specific obstacle is
prone to ‘‘share the pain’’ with partners, the joint experience of several barriers does not
exert a cumulative effect since there is no intensification of cooperation.
The sub-modularity effect is especially evident in the case of the joint presence of cost
or market obstacles with knowledge-related barriers: lack of adequate information on
technologies or of appropriate skills seem to be the problems which, when experienced
jointly with other types of bottlenecks, lead firms to increase cooperation less than in
perception of a single barrier as relevant.
Overall, we would argue that prior lack of absorptive capacity in firms tends to
exacerbate the perception of barriers of different types, e.g., cost-related barriers, cause firms to
reduce their reliance on external partners in the joint presence of various types of obstacles.
Our paper adds to the literature on absorptive capacity, with the aim of including the role of
the barriers to innovation as potential drivers of cooperation depending on a minimum
level of absorptive capacity in the firm.
Our findings have implications for policy if the priority is to enhance cooperation
among firms, and with research organizations and especially, from the perspective of a
strategy to cope with the obstacles to innovation.
More generally, borrowing from a systems failure (rather than a market failure)
framework for innovation policy (e.g. Metcalfe 2005; Woolthuis et al. 2005), we would
argue that the presence and effect of financial and non-financial obstacles to innovation
equate to many of the several dimensions of systemic failures. Soft institutional failure
might encompass lack of an adequate financial system for firms to access not excessively
costly funds for innovation investments. Capability failures refer to the consequences of
skills and knowledge shortages that firms might experience when engaging in innovation.
Of most interest is that the framework refers to interaction failures, which encompass a
wide variety of inter-firm connection failures including strong and weak network failures,
lack of weak ties and myopia due to internal orientation (Woolthuis et al. 2005).
Our evidence suggests that cooperation for innovation is intensified as a result of a range
of systemic failures and, therefore, emerges as a coping strategy in the presence of relevant
barriers. This result should help policy makers to address systemic failures by selectively
supporting cooperation and deal with what Woolthuis et al. (2005) refer to as interaction
failures. This perspective supports the view proposed by others (Coad et al. 2016), that
policy intervention to reduce barriers (e.g., by supporting and facilitating risk and
costreducing cooperation) should not be ascribed to market failure fixing, but rather to a
selective, deliberate and systematic intervention to address systemic failure. In this respect,
we hope that further research will investigate more directly the real effectiveness of
system-oriented policies to mitigate the barriers to innovation for firms.
More generally, more research is needed to disentangle the relation between innovation
barriers and cooperation strategies. In this context, the availability of panel data could lead
to better analysis of the causal framework and feedback mechanisms between obstacles
and collaboration. An additional point concerns the availability of data on cooperation and
barriers. Our dataset does not provide fine-grained information to capture the intensity of
obstacles (e.g., in a continuous way) and collaboration (e.g., resources committed) or the
number of different collaborations with a specific type of partner (e.g., the number of
different suppliers with which the firm cooperates). We hope that future research will
capture these aspects.
Acknowledgements Previous versions of the paper were presented at the 2014 EUSPRI (Manchester, UK),
2013 ENEF (Madrid, SP). Maria Savona gratefully acknowledges the Horizon2020 Grant on Innovation-led,
Sustainable and Inclusive Growth (ECRN 194562).
Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
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and reproduction in any medium, provided you give appropriate credit to the original author(s) and the
source, provide a link to the Creative Commons license, and indicate if changes were made.
Ahuja , G. ( 2000 ). Collaboration networks, structural holes, and innovation: A longitudinal study . Administrative Science Quarterly , 45 , 425 - 455 .
Antonioli , D. , Mancinelli , S. , & Mazzanti , M. ( 2013 ). Is environmental innovation embedded within highperformance organisational changes? The role of human resource management and complementarity in green business strategies . Research Policy , 42 , 975 - 988 .
Arrow K. ( 1962 ). Economic welfare and the allocation of resources for invention . In Universities-National Bureau Committee for Economic Research, Committee on Economic Growth of the Social Science Research Council . (Eds.), The rate and direction of inventive activity: Economic and social factors (pp . 609 - 626 ). Princeton: Princeton University Press.
Arvanitis , S. ( 2012 ). How do different motives for R&D cooperation affect firm performance? An analysis based in Swiss micro data . Journal of Evolutionary Economics , 22 , 981 - 1007 .
Baldwin , J. , & Lin , Z. ( 2002 ). Impediments to advanced technology adoption for Canadian manufacturers . Research Policy , 31 , 1 - 18 .
Barge-Gil , A. ( 2010 ). Cooperation-based innovators and peripheral cooperators: An empirical analysis of their characteristics and behavior . Technovation , 30 , 195 - 206 .
Becker , W. , & Dietz , J. ( 2004 ). R&D cooperation and innovation activities of firms: Evidence from the German manufacturing industry . Research Policy , 33 , 209 - 223 .
Belderbos , R. , Carree , M. , & Lokshin , B. ( 2004 ). Cooperative R &D and firm performance. Research Policy , 33 , 1477 - 1492 .
Belderbos , R. , Carree , M. , & Lokshin , B. ( 2006 ). Complementarity in R&D cooperation strategies. Review of Industrial Organisation , 28 , 401 - 426 .
Caloghirou , Y. , Ioannides , S. , & Vonortas , N. ( 2003 ). Research joint ventures . Journal of Economic Surveys , 17 , 541 - 570 .
Canepa , A. , & Stoneman , P. ( 2008 ). Financial constraints to innovation in the UK: Evidence from CIS2 and CIS3 . Oxford Economic Papers, 60 , 711 - 730 .
Cassiman , B. , & Veugelers , R. ( 2002 ). R&D cooperation and spillovers: Some empirical evidence from Belgium . American Economic Review , 92 , 1169 - 1184 .
Coad , A. , Pellegrino , G. , & Savona , M. ( 2016 ). Barriers to innovation and firm productivity . Economics of Innovation and New Technology , 25 , 321 - 334 .
Cohen , W. M. , & Levinthal , D. ( 1989 ). Innovation and learning: The two faces of R&D. The Economic Journal , 99 , 569 - 596 .
Cohen , W., & Levinthal , D. ( 1990 ). Absorptive capacity: A new perspective on learning and innovation . Administrative Science Quarterly, 35 , 128 - 152 .
Colombo , M. , & Garrone , P. ( 1996 ). Technological cooperative agreements and firm's R&D intensity. A note on causality relations. Research Policy , 25 , 923 - 932 .
D' Este , P. , Guy , F. , & Iammarino , S. ( 2013 ). Shaping the formation of university-industry research collaborations: What type of proximity does really matter ? Journal of Economic Geography , 13 , 537 - 558 .
D' Este , P. , Iammarino , S. , Savona , M. , & Von Tunzelmann , N. ( 2008 ). What hampers innovation? Evidence from UK CIS4 . SPRU electronic working paper series (SEWPS) , no. 168 (February).
D' Este , P. , Iammarino , S. , Savona , M. , & Von Tunzelmann , N. ( 2012 ). What hampers innovation? Revealed barriers versus deterring barriers . Research Policy , 41 , 482 - 488 .
D' Este , P. , Rentocchini , F. , & Vega Jurado , J. ( 2014 ). Lowering barriers to engage in innovation: Evidence from the Spanish Innovation Survey . Industry and Innovation , 21 , 1 - 19 .
David , P. , & Hall , B. ( 2000 ). Heart of darkness: Modelling public private funding interactions . Inside the R&D black Box. Working Paper 7538 . http://www.nber.org/papers/w7538
De Faria , P. , Lima , F. , & Santos , R. ( 2010 ). Cooperation in innovation activities: The importance of partners . Research Policy, 39 , 1082 - 1092 .
Franco , C. , Marzucchi , A. , & Montresor , S. ( 2014 ). Absorptive capacity, proximity in cooperation and integration mechanisms . Empirical evidence from CIS data. Industry and Innovation , 21 , 332 - 357 .
Fritsch , M. , & Lukas , R. ( 2001 ). Who cooperates on R&D? Research Policy , 30 , 297 - 312 .
Galia , F. , & Legros , D. ( 2004 ). Complementarities between obstacles to innovation: Evidence from France . Research Policy, 33 , 1185 - 1199 .
Gallie ´ , E.-P. , & Roux , P. ( 2010 ). Forms and determinants of R&D collaborations: Evidence based on French data . Industry and Innovation , 17 , 551 - 576 .
Garcia-Quevedo , J. , Pellegrino , G. , & Savona , M. ( 2014 ). Reviving demand-pull perspectives: The effect of demand uncertainty and stagnancy on R&D strategy. SPRU Working Paper 2014 - 09 .
Hagedoorn , J. , Link , A. N. , & Vonortas , N. S. ( 2000 ). Research partnerships . Research Policy, 29 , 567 - 586 .
Hall , B. H. , Moncada-Paterno `- Castello , P. , Montresor , S. , & Vezzani , A. ( 2016 ). Financing constraints, R&D investments and innovative performances: New empirical evidence at the firm level for Europe . Economics of Innovation and New Technology , 25 , 183 - 196 .
Ho¨lzl, W., & Janger , J. ( 2013 ). Does the analysis of innovation barriers perceived by high growth firms provide information on innovation policy priorities? Technological Forecasting and Social Change , 80 , 1450 - 1468 .
Ho¨lzl, W., & Janger , J. ( 2014 ). Distance to the frontier and the perception of innovation barriers across European countries . Research Policy , 43 , 707 - 725 .
Hottenrott , H. , Rexha¨user , S. , & Veugelers , R. ( 2014 ) Green innovations and organizational change: Making better use of environmental technology . ZEW-discussion paper 12-043
Iammarino , S. , Sanna-Randaccio , R. , & Savona , M. ( 2009 ). The perception of obstacles to innovation. Foreign multinationals and domestic firms in Italy. Revue d'e´ conomie Industrielle , 125 , 75 - 104 .
Kaiser , U. ( 2002 ). An empirical test of models explaining research expenditures and research cooperation: Evidence for the German service sector . International Journal of Industrial Organization , 20 , 747 - 774 .
Kleinknecht , A. , & Reijnen , J. O. N. ( 1992 ). Why do firms cooperate in R&D? An empirical study . Research Policy , 21 , 347 - 360 .
Laursen , K. , & Salter , A. J. ( 2014 ). The paradox of openness: Appropriability, external search and collaboration . Research Policy, 43 , 867 - 878 .
Lhuillery , S. , & Pfister , E. ( 2009 ). R&D cooperation and failures in innovation projects: Empirical evidence from French CIS data . Research Policy, 38 , 45 - 57 .
Lo´pez, A. ( 2008 ). Determinants of R&D cooperation: Evidence from Spanish manufacturing firms . International Journal of Industrial Organisation , 26 , 113 - 136 .
Mairesse , J. , & Mohnen , P. ( 2010 ), Using innovation surveys for econometric analysis . UNU-MERIT working paper series , 2010 - 023 .
Mancusi , M. L. , & Vezzulli , A. ( 2010 ) R&D, innovation, and liquidity constraints. KITeS working papers 30/ 2010 , Bocconi University.
Marzucchi , A. , Antonioli , D. , & Montresor , S. ( 2015 ). Industry-research co-operation within and across regional boundaries . What does innovation policy add? Papers in Regional Science ., 94 , 499 - 524 .
Metcalfe , J. S. ( 2005 ). Systems failure and the case for innovation policy . In P. Llerena & M. Matt (Eds.), Innovation policy in a knowledge-based economy (pp . 47 - 74 ). Berlin: Springer.
Milgrom , P. , & Roberts , J. ( 1995 ). Complementarities and fit: Strategy, structure, and organizational change in manufacturing . Journal of Accounting and Economics , 19 , 179 - 208 .
Miotti , L. , & Sachwald , F. ( 2003 ). Co-operative R&D: Why and with whom? An integrated framework of analysis . Research Policy , 32 , 1481 - 1499 .
Mohnen , P. , & Hoareau , C. ( 2003 ). What type of enterprise forges close links with universities and government labs? Evidence from CIS2 . Managerial and Decisions Economics , 24 , 133 - 145 .
Mohnen , P. , & Ro¨ller, L.- H. ( 2005 ). Complementarities in innovation policy . European Economic Review , 49 , 1431 - 1450 .
Mohnen , P. , & Rosa , J. M. ( 2002 ). Barriers to innovation in service industries in Canada . In M. Feldman & N. Massard (Eds.), Institutions and systems in the geography of innovation (pp . 231 - 250 ). New York : Springer.
Mora Valent´ın , E. M. ( 2000 ). University-industry cooperation: a framework of benefits and obstacles . Industry and Higher Education , 14 , 165 - 172 . doi:10.5367/000000000101295011.
Mowery , D. C. , Oxley , J. E. , & Silverman , B. S. ( 1998 ). Technology overlap and interfirm cooperation: Implication for the resource-based view of the firm . Research Policy , 27 , 507 - 523 .
Pellegrino , G. , & Savona , M. ( 2013 ). Is money all? Financing versus knowledge and demand constraints to innovation . UNU-MERIT WP series.
Robin , S. , & Schubert , T. ( 2013 ). Cooperation with Public Research Institutions and success in innovation: Evidence from France and Germany . Research Policy, 42 , 149 - 166 .
Savignac , F. ( 2008 ). Impact of financial constraints on innovation: What can be learned from a direct measure ? Economics of Innovation and New Technology , 17 , 553 - 569 .
Schmidt , T. ( 2005 ). What determines absorptive capacity? Unpublished manuscript , ZEW, Mannheim.
Segarra-Blasco , A. , & Arauzo-Carod , J.-M. ( 2008 ). Sources of innovation and industry-university interaction: Evidence from Spanish firms . Research Policy , 37 ( 8 ), 1283 - 1295 .
Tether , B. ( 2002 ). Who cooperates for innovation and why: An empirical analysis . Research Policy , 31 , 947 - 967 .
Tether , B. , & Tajar , A. ( 2008a ). Beyond university-industry links: Sourcing knowledge for innovation from consultants, private research organisations and the public science-base . Research Policy, 37 , 1079 - 1095 .
Tether , B. , & Tajar , A. ( 2008b ). The organizational-cooperation mode of innovation and its prominence amongst European service firms . Research Policy , 37 , 720 - 739 .
Tiwari , A. , Mohnen , P. , Palm , F. , & Schim van der Loeff, S. ( 2008 ). Financial constraint and R&D investment: Evidence from CIS. In Determinants of innovative behaviours: A firm's internal practice and its external environments (pp . 217 - 242 ), London.
Veugelers , R. , & Cassiman , B. ( 1999 ). Make and buy in innovation strategies: Evidence from Belgian manufacturing firms . Research Policy , 28 , 63 - 80 .
Veugelers , R. , & Cassiman , B. ( 2005 ). R&D cooperation between firms and universities. Some empirical evidence from Belgian manufacturing . International Journal of Industrial Organization , 23 , 355 - 379 .
Woolthuis , R. K. , Lankhuizen , M. , & Gilsing , V. ( 2005 ). A system failure framework for innovation policy design . Technovation , 25 , 609 - 619 .
Wu , J. ( 2012 ). Technological collaboration in product innovation: The role of market competition and sectoral technological intensity . Research Policy , 41 , 489 - 496 .
Zahra , S. , & George , G. ( 2002 ). Absorptive capacity: A review, re-conceptualization and extension . Academy of Management Review , 27 , 185 - 203 .