Organizational diversity and innovation potential of EU-funded research projects
Organizational diversity and innovation potential of EU-funded research projects
Daniel Nepelski 0 1 2
Giuseppe Piroli 0 1 2
JEL Classification L 0 1 2
0 DG Employment, Social Affairs and Inclusion, European Commission , Brussels , Belgium
1 Joint Research Centre, European Commission , Calle del Inca Garcilaso 3, 41092 Seville , Spain
2 & Daniel Nepelski
Impact evaluations of collaborative research projects usually focus on private benefits of participants, e.g. their turnover or employment growth. We study the innovative performance of collaborative research projects and how it depends on the organizational diversity of participating organizations. Our population includes participants to EC-funded collaborative research projects that are considered as key organisations behind delivering innovations. The focus on innovative rather than, for example, financial outcomes allows us to assess the transformative effect of publically-funded collaborative research. We show that the innovative potential of research output of homogenous partnerships, e.g. between two SMEs or two large companies, is likely to be higher, as compared to heterogeneous partnerships, e.g. an SME and a large company. The impact of universities on the potential of innovations is unclear. The total number of key organizations in delivering an innovation has negative impact on its potential. Neither project funding nor duration affects the potential of innovation. Our results implicitly show that, depending on the type of organization and consortium design, there are different incentives to contribute to innovative efforts and opportunities to appropriate their benefits.
Collaborative R&D projects; Innovation policy; Framework programme; Small and medium-sized enterprises; Universities; European; Commission
The views expressed are those of the authors and may not in any circumstances be regarded as stating an
official position of the European Commission.
The European Union’s (EU) Framework Programme (FP) constitutes an important share in
R&D expenditures in Europe
.1 In addition to finance science and technology
development, one of their main objectives of the FP is to foster international collaboration
among research organizations and private firms, both large and SMEs. For example, the
Cooperation program was the core of the 7th FP and represented two-thirds of its overall
budget. It aimed at fostering collaborative research across Europe and other partner
countries through projects by transnational consortia of industry and academia. The
objective of the FP is increasingly shifting from sponsoring basic research to becoming a
main factor behind economic and social transformation (EC 2015). This transformation
takes place through enhancing the application of scientific results to solve known problems
and to increase the commercialization of technology
(Leyden and Link 2015; Mazzucato
. Public sector entrepreneurship triggers the transformation primarily by increasing
the effectiveness of knowledge networks; that is, by increasing the heterogeneity of
experiential ties among economic units and the ability of those same economic units to
exploit such diversity
(Audretsch and Link 2016)
This paper tackles the issue of innovation potential of FP7 research projects and how it
is related to the organization diversity of organizations involved in the development of
innovations. So far most of impact assessments of FP programs are limited to the
accounting for scientific output and filled patent applications
analyze benefits to participating organizations. Assessment studies focus mainly on
profitability or employment change of participating firms
(Aguiar and Gagnepain 2013;
Barajas et al. 2012; Bayona-Sa´ez and Garc´ıa-Marco 2010; Belderbos et al. 2004)
are few attempts that go beyond this by analyzing, for example, social benefits
1999; Link and Scott 2004)
. Hence, by focusing on the innovative output, we introduce a
new way of analysing the impact of EC-funded research projects.
Regarding the issue of organizational diversity, by distinguishing between different
organizational forms, we introduce the notion of inequality in terms of input and effort
performed by various organizations with respect to innovating in publically-funded
research projects. This can be related to unequal incentives or appropriation opportunities
related to the organizational form of participants
(Ro¨ ller et al. 2007)
We proceed in two steps. First, we use the output of the assessment of innovation
potential of over 500 innovations identified in FP7 research projects in the domain of
information and communication technologies (ICT). This is done using a formal
innovation potential assessment framework and aggregating answers to a novel innovation survey
questionnaire used in the assessment of FP7 projects. The aggregated indicators capture the
level of innovation readiness, management and market potential and through a composite
indicator the overall potential of an innovation. Second, we examine the relationship
between the potential of innovations and the diversity of partnerships involved in the
development of these innovations. By partnership we mean the type of organizations that
were identified by reviewers of the FP7 projects as ‘‘key organisation(s) in the project
delivering an innovation’’. We distinguish between homogenous, e.g. two universities, or
two SMEs, and heterogeneous partnerships, e.g. at least one university and one SME, or at
least one SME and one large company.
1 The FP7 has a budget of over €50 billion with €9 billion allocated to ICT for the period from 2007 to 2013
. In comparison, the ICT sector R&D annual expenditures in the EU reached almost €30 billion in
2011 (JRC 2014).
In this paper, we use data provided by the Innovation Radar (IR) project, an EC support
initiative launched in August 2013
(De Prato et al. 2015)
. In its first release, the IR project
collected data between May 2014 and January 2015 on 279 ICT FP7 and Competitiveness
and Innovation Framework Programme (CIP) projects or 10.6% of all ICT FP7/CIP
projects. According to the first findings, an average ICT FP7 project produces 2 innovations.
However, their commercialisation potential varies and further nurturing is needed to
exploit their potential
(De Prato et al. 2015)
. Access to financing, IPR and regulation are
among the factors that are considered as major bottlenecks to commercial exploitation of
those innovations. At the same time, partnership with other company, expanding to more
markets and business plan development are among the most frequent needs of
organizations identified as key innovators.
The results show that the design of project consortia is more important than project
funding or duration. On the one hand, this has some implications for organizations
participating to collaborative research projects. On the other hand, it provides some insights
on how to help consortia to commercialise their innovations by providing support that
takes into account the characteristics of the participating organizations.
The current paper is structured as follows: Sect. 2 reviews some key findings of the
existing evidence on the performance of R&D partnerships and formulates the research
questions that we tackle. Section 4 explains the topic of assessment of innovation and
technology-based ventures and the methodology of constructing innovation potential
assessment indicators and presents data used in the current study. Section 5 show a
descriptive analysis of innovations and Sect. 6 present the results of the innovation
potential assessment. Section 7 concludes.
2 Literature review
2.1 The rationale for public support to R&D collaboration
R&D collaboration increases a firm’s incentives to perform some types of R&D activity,
mainly with results difficult to be appropriated
. Joint R&D efforts minimise
issue of appropriation of R&D outcomes and increase private benefits of a firm. Companies
are willing to join forces provided that they can access to complementary resources
(Caloghirou et al. 2001; Kogut 1988; Sakakibara 1997)
, overcome transaction costs, or
reduce risk associated with uncertain R&D outcomes
(Hagedoorn et al. 2000)
collaborations do not only benefit firms involved in such activities. The existence of larger
collaboration networks increases also the innovation performance of individual locations
(Asheim et al. 2011; T o¨dtling and Trippl 2005)
. Thus, considering the positive
private and social benefits of R&D collaboration, the general conclusion is that there is
room for public intervention in overcoming the problems of coordination and risk sharing
in knowledge production (Davenport et al. 1998). Therefore, one of the main features of
the Framework Programmes of the EC is an increasing emphasis on collaborative research,
both within the EU and with external research partners.
Besides the additionally effect of publically-sponsored research joint-ventures (RJVs),
there is also the issue of their directionality
. In the perspective of the
public sector entrepreneurship, the European collaborative R&D projects initiatives are
expected to trigger technological and economic transformation by creating and exploiting
networks that generate socially desired innovations
(Leyden 2016; Link 2016)
. The public
sector seeks to increase the effectiveness of knowledge networks and gives rise to a
discovery process by which organizations attempt to bring the desired innovation to the
market and or society
(Audretsch and Link 2016)
2.2 The role of diversity in collaborative research projects
Diversity is expected to play a positive role in collaborative research projects by increasing
the level of novelty and facilitating discovery of cross-border applications and solutions. At
organization level, rooted in different experiences, culture, organizational form,
technological endowments, diversity promotes constructive exchange of capabilities,
technological and administrative innovation and entry into new product markets
Cox 1993; Knight et al. 1999; Perkins and Fields 2010)
. It leads to greater variance in
ideas, creativity, and innovation. Diversity opens up new perspectives and opportunities to
expand into new geographic locations (Barkema and Shvyrkov 2007). According to
Dia´nez-Gonza´lez and Camelo-Ordaz (2016)
, diversity influences the level of entrepreneurial
orientation. The presence of non-academic managers is a key factor in the academic
spinoffs’ higher levels of entrepreneurial orientation. Demographic and cultural diversity is
also related to the level of novelty of new ventures. A high level of innovativeness requires
frequent and rich interactions among members of an organization. Similarity facilitates
exchange of subjective and ambiguous information while maintaining unity and continuity
of purpose. At the same time, to learn, recognize and accommodate new opportunities, a
team must be able to span the boundaries between itself and its environment. Its members
must represent an array of diverse talents and capabilities. Such breadth facilitates learning
and adaptation and is driven by dissimilarity
(Amason et al. 2006)
. The benefits of
diversity come at a cost. Under some circumstances, the coordination costs may outweigh
the positive ones
(Ancona and Caldwell 1992)
2.3 Diversity and performance of publically funded research projects
Against this background, one of the key questions in facilitating European knowledge
networks is the issue of their composition and structure. FP encourages collaboration
between public and private organization and increasing emphasises the involvement small
and medium size enterprises (SEMs)
(Caloghirou et al. 2001; Santoro and Chakrabarti
. It is argued that small firms participate in larger R&D project that involve, among
others, universities in order to get access to novel knowledge and technology and to benefit
from spillovers (Chun and Mun 2012). Additional benefits of R&D collaboration between
firms and universities include increased productivity
(Cunningham and Link 2015; Link
and Rees 1990)
; higher probability of commercialisation R&D results
et al. 2017; Link and Ruhm 2009)
, and a business’s economies of technological scope
increase with university involvement
(Leyden and Link 2013, 2015)
However, collaboration between partners of various sizes and backgrounds does not
happen smoothly. One of the reasons is diverging interests, motivations to join a
consortium and incentives to provide input to common project. SMEs report a strong strategic
alignment with FP projects and explicit goals related to innovation outputs such as
developing a prototype, a patentable technology, or a complementary technology that will
directly enhance their competitiveness
(Polt et al. 2008)
. They focus on projects with an
applied orientation and engage only in cooperative agreements that are likely to yield
tangible benefits guaranteeing them immediate survival and growth. In contrast, large firms
appeared much less inclined to commercialise right out of the project, compared to highly
committed-to-commercialisation SMEs. Because of the more marginal role of FP projects,
larger companies reported weaker strategic alignment and less explicit goals. Participation
in collaborative R&D projects is meant as technology watch, acquisition of new knowledge
and building partnerships
(Hernan et al. 2003)
. Universities, on the other hand, seek in
RJVs complementary resources that allow them to advance research
(Caloghirou et al.
2001; Polt et al. 2008)
. Based on their scientific capabilities, universities engage in
research collaborations on the basis of research contents (Carayol 2003). The main
criterion for collaboration is that it feeds of a university’s own research agenda
. Commercialisation is not the main objective but rather the building up of new
knowledge and technology and the investigation of new research areas.
Considering that different types of organizations have different objectives and
incentives to be involved into RJV, we can assume that these differences are likely to affect the
inputs from various partners, the outcomes of collaboration and how their results are
appropriated by among them
(Link 2015; Link and Siegel 2005)
. For example, large firms
are less willing to share their economic knowledge with smaller rivals and have a
preference to collaborate with other large firms in order to maximize the internalization of
(Ro¨ ller et al. 2007)
. Diversity in firm size and efficiency level, can also impede
effective R&D collaboration
. Similar considerations apply to collaborations
between companies and universities
(Bronwyn et al. 2003)
. Participation of universities in
RJV does not seem to increase technological performance of the project and the outcomes
depend on the company size
. In general, SMEs can benefit more from
R&D collaboration with universities rather than larger firms. Hence, although European
research networks are characterised with institutional diversity, large industrial partners
represent only a small share of the all participants
(Pandza et al. 2011)
Although the FP seeks variety in size, organizational type and geographical location of
participants to grow the diversity of research networks, this increases coordination cost and
creates managerial challenges in communicating and sharing knowledge across national
and institutional borders. Overall, this may not be desired by all partners and does not
guarantee a successful collaboration. Therefore, the design of a consortium is more
important than the level of R&D input in explaining the technological performance
(Branstetter and Sakakibara 2002; Kastrinos 1994)
. In particular, such organizational
characteristics of consortia as technological and product market proximity of members,
level of centralization, diversity of members etc., are particularly important for the positive
outcomes of such collaborations.
3 Research questions
Taking into account the above discussion, the question we address in this paper concerns
the relationship between innovative performance of research projects and organizational
diversity of RJVs. Existing research efforts analysing the impact of RJVs, including
publically-funded, focus on, for example, organization-level outcomes, e.g. productivity
and employment increases
(Barajas et al. 2012; Bayona-Sa´ez and Garc´ıa-Marco 2010)
Instead, we look at the innovation output of publically-funded RJVs. The focus on
innovative output links the analysis to the strand of literature on public sector entrepreneurship
(Leyden and Link 2015; Mazzucato 2013)
. This perspective views the role of public sector
and technology and innovation policy not only as a sponsor of basic research, but as a main
factor behind economic and social transformation. This transformation is expected through
directed innovation relying on the application of scientific results to solve known problems
and to increase the commercialization of technology. Our study differs also from existing
ones by observing project-level rather than individual performance.
Recognizing that there is a link between design of RJVs and their performance, we
specifically look at the relationship between the diversity of organizations involved in
developing an innovative product or service within publically-funded research project and
its innovation potential. Regarding organizational diversity, we distinguish between
homogenous, e.g. university and university or SME and SME, and heterogeneous, e.g.
university and large company or large company and SME, partnerships. We are interested
in the question of which type of partnerships are associated with higher innovation
potential of R&D outcomes. Innovation potential is related to innovation readiness,
management and market potential. By looking at both the aggregate score of the innovation
potential composite indicator and the sub-composite indicators, we are additionally
interested in the question of how the composition of innovation partnership perform with
respect to such elements of the process of technology commercialisation as innovation
readiness, management and market potential.
4 Methodology and data
This section describes the methodology applied in this paper. It uses the output of the
Innovation Radar (IR) project, an EC support initiative to assess the innovation potential of
innovations developed within the FP research projects and identify the bottlenecks to their
(De Prato et al. 2015)
. Below we explain the innovation potential
assessment criteria used in the current study (Sect. 4.1) and the measures of diversity of
innovation partnerships (Sect. 4.2).
4.1 Innovation potential assessment framework
The principles of the IR rest on the concept of innovation and new technology venture
assessment. This type of activity is commonly performed by large research organizations,
technology-based companies, universities or venture capitalists screening companies or
projects with respect to their new product development, technological readiness and market
potential of new products
(De Coster and Butler 2005; Liao and Witsil 2008)
. In general
terms, one can differentiate between two types of assessment of new innovations and
technology projects. One is a process-based and the other culturally-based
Kleinschmidt 1997; Khurana and Rosenthal 1998)
. Table 1 provides a synthesis of the
main characteristics of the two approaches.
The process-based assessment uses established procedures for assessing proposals for
funding. It is mainly used by, for example, banks granting loans to small, technology-based
enterprises, or large research organizations, e.g. the National Aeronautics and Space
Administration (NASA), when choosing new products to develop from various
technological projects. The process-based assessment tends to be regular, with proposals arriving
and being reviewed on a methodological basis. A regular process warrants an investment in
methods and tools that lend themselves to comparing several options simultaneously and
that keep records so that future opportunities can be compared with past opportunities. In
contrast, the culturally-based approach does not assess all projects against a formal
methodology. Instead, assessment is based on the assessor’s experiences both individually
and collectively. Business angels and venture capitalists are the most common users of the
culturally-based approach to assessing new technology ventures. The assessment is usually
done on a case-by-case basis by a team consisting of experts with different backgrounds.
Within this framework, the IR methodology can be seen as a process-based approach to
innovation and new technology assessment. It applies a structured framework to assessing
the potential of innovations and innovative capacity of organisations that play a key role in
delivering these innovations.
In order to provide synthetic comparable results for further analysis and interpretation,
the IR innovation potential assessment framework uses three assessment criteria that are
commonly referred to in the context of innovation potential assessment exercises: Market
Potential, Innovation Readiness and Innovation Management
(De Coster and Butler 2005;
Liao and Witsil 2008)
. These three assessment criteria are expected to capture the potential
value an innovation can generate, its market readiness and, finally, the quality of the
commercialisation process. The choice of these three elements as the determinants of
innovation potential is motivated as follows: Regarding the market potential, a
commercially viable innovation must demonstrate economic benefit. The greater the benefit, the
more desirable and marketable an innovation is. At the same time, innovation
commercialization process involves acquiring ideas about existing or potential market needs and
looking for solutions satisfying them (Mitchell and Singh 1996). Thus, market potential
reflects the likely economic or social value that can be generated by a new product or
(de Vries 2012)
. With respect to innovation readiness, successful launch of
innovative products or services begins with the identification of technologies that are ready for
(Galbraith et al. 2006; Heslop et al. 2001)
. Frequently, innovation
potential of innovations is assessed as low for novel technologies, at early stages of
development. This is particularly true for the outcomes of research in universities and
research institutes (Richard and Thursby 2001). Majority of such innovations are so
underdeveloped that no one knows their commercial potential. Innovation readiness
criterion refers to the technical maturity of an evolving innovation
(Heslop et al. 2001)
Finally, innovation management captures the level of a project participants’ commitment
to bring an innovation to the market, which is one of the key factors behind successful
(Kirchberger and Pohl 2016; Meseri and Maital 2001)
fact that the success of research collaborations depends largely on management issues was
also confirmed for EU collaborative projects (Devenport et al. 1999). Below each criterion
is described in a greater detail.
Innovation readiness This concept is related to the ‘‘technology readiness levels’’
(TRLs) framework introduced in the mid-1970s by NASA
. By being
discipline-independent, it was expected to allow more effective assessment of the maturity
of new technologies. TRLs have been embraced by both private and public organizations,
as it allows for highly effective communication of the status of new technologies among
sometimes diverse organizations. Factors related to innovation readiness include, among
others, the quality of the technology, age, scope, pioneering nature, and expected time to
market. It aims to define the development phase of the innovation, e.g. conceptualization,
experimentation or commercialisation. It also takes into account the steps that were taken
in order to prepare innovation for commercialisation, e.g. prototyping, demonstration or
testing activities or a feasibility study, and to secure the necessary technological resources,
e.g. skills, to bring the innovation to the market. In addition, this criterion takes into
account the development stage of an innovation and the time to its potential
Innovation management Innovation Management is related to the assessment and
management of risks related to innovation commercialisation. It involves such measures as
securing resources, organizing the process and setting milestones for technology transfer.
This requires commitment from the top management
. Also interactions with
external actors, e.g. potential customers or users, increase the changes of a successful
commercialization of technologies
(Gerard et al. 2002)
. Thus, the concept of innovation
management aims to capture the capability of the project’s development and/or
management team to execute the necessary steps to transform a novel technology or research
results into a marketable product and, finally, to prepare its commercialisation. These steps
may include, for example, clarifying the related ownership and IPR issues, preparing a
business plan or market study, securing capital investment from public and/or private
sources, or engaging an end-user in the project.
Market potential Market potential criterion relates to the demand and supply side of an
innovation. Regarding the demand side, it concerns the prospective size of the market for a
product and the chances of its successful commercialisation. Its aim is to assess how the
product satisfies a market sector and to indicate that there is potential customer base.
Market size and dynamics are among the most relevant factors behind a successful
(Meseri and Maital 2001)
. With respect to the supply side, it
aims to assess whether there are potential barriers, e.g. regulatory frameworks or existing
IPR issues, which could weaken the commercial exploitation of an innovation. In the
current undertaking, the focus is placed on the supply side. This is mostly related to the fact
that information on markets for individual innovations is not available.
In order to observe and measure the above specified criteria, each of them was matched
with relevant questions of the Innovation Radar Questionnaire (see Sect. 8). The outcome
of the matching process is presented in Table 5 (see Sect. 8.2). Composite sub-indicators
for each assessment criterion were recreated, i.e. Innovation Readiness Indicator (IRI),
Innovation Management Indicator (IMI), and Market Potential Indicator (MPI). Each of the
three indicators is an arithmetic aggregate of all relevant information in the domain of
innovation readiness as defined in Sect. 4.1 and scoring system presented in Table 5 in
Sect. 8.2. In the second step, the Innovation Potential Indicator (IPI) is constructed. IPI is
an arithmetic composite indicator which aggregates the values of the sub-indicators.
An important issue related to the construction of composite indicators is the one of
weighting. Unfortunately, no agreed methodology exists to weight individual indicators
. In particular the context of the current study does not make the choice of a
weighting scheme easy. All three elements are considered equally important for a
successful innovation commercialization. Considering this, equal weighting is applied as
IPI ¼ 3 IRI þ 3 IMI
In order to make the values on each indicator among different innovations and
innovators as easily comparable as possible, a normalisation procedure is applied. Observed
values of each indicator are brought to the scale between 0 and 100 in the following way:
Ii Normalized Score ¼
Ii Observed Score
Ii Max Score
where Ii is one of the innovation potential assessment indicators specified above.
4.2 Organizational diversity
In our study, we use different concept of organizations participating in innovation
partnerships. Instead of relying on administrative information on project consortia, we use
information on organizations that were identified by experts during project reviews as ‘‘key
organisation(s) in the project delivering an innovation’’ (see the Innovation Radar
innovation questionnaire in Sect. 8.1). The rationale behind identifying organizations in this
way is to point at individual organizations among the consortium partners that play the
most relevant role in innovation development. This way, our population includes
participants to the FP7 projects that are considered as the main drivers of development of new
technologies and innovations. This feature is unique among the studies analysing
collaboration in research projects, which assume equal efforts and opportunities to appropriate
their results. It introduces the notion of inequality and different incentives of organizations
to participate and contribute to the consortium.
The project reviewers can identify up to three organizations per innovation. According
to the FP procedures, there are five types of organizations that are eligible to participate to
the research projects: High Education and Schools and Research Centres (HES/REC);
Public Bodies (PUB); Small Medium Enterprise (SMEs); Large companies (LARGE) and
Other organisations (NIL)
. Based on this classification and on the fact that the
IR provides information on up to three organizations involved in the development and
delivering of an innovation, we distinguish between:
Homogenous innovation partnerships, e.g. university and university or SME and SME,
Heterogeneous innovation partnerships, at least one university and one SME, or at least
one SME and one large company.
In addition, in order to control for the size of a partnership, in the proceeding analysis, we
use a variable controlling for the number of key organizations to deliver the innovation.
The data used in the current project was collected during periodic reviews of ICT FP7/CIP
projects between 20 May 2014 and 19 January 2015 (see Table 2). The reviews were
conducted by external experts commissioned by DG Connect. During this time, in addition
to a standard review procedure, DG Connect deployed the Innovation Radar questionnaire
(see Sect. 8) to spot innovations originating from the FP7 projects and the key
organizations behind them. The research activities monitored are the ICT research actions and the
e-Infrastructures activity under the Seventh Framework Programme 2007–2013 (under
Cooperation and Capacities themes), and the policy support actions carried out under the
Competitiveness and Innovation Framework Policy Support Programme (CIP ICT PSP).
In order to complement the survey data, information on FP7 projects’ characteristics
was retrieved from the CORDIS database
. This database is the European
Commission’s public repository of information on all EU-funded research projects and
their results. For the purpose of this study we retrieved, among others, information on the
type and location of organizations that were identified as key organizations to bring the
innovations to the market, EC funding and duration.
5 Descriptive analysis
According to Table 2, between May 2014 and January 2015, 279 projects were reviewed
using the IR Questionnaire, i.e. 10.6% of all ICT FP7, e-Infrastructures and CIP ICT PSP
. As a result, 517 innovations were identified. This means
that, on average, an ICT FP7/CIP project produces nearly 2 innovations. The number of
distinct organizations considered as key organisations in the project delivering these
innovations amounted to 544. The average number of innovators per innovation was 1.23.
Table 3 reports the summary statistics of the three innovation potential assessment
subindicators, i.e. Innovation Readiness (IRI), Innovation Management (IMI), Market
Potential (MPI) and the composite Innovation Potential (IPI), for all analysed innovations and by
innovation potential category. In addition, we show details on the key organizations in the
project delivering an innovation, as identified during project reviews, and such project
features as duration in months and total EC funding in Euro.
Data: European Commission DG Connect
The table includes computations on innovation potential assessment indicators as defined in Sect. 4.1. Total
number of reviewed projects: 279. Total number of innovations: 517. Review period: 20.05.2014 and
The average value of the IPI among all the innovations is 45.52 out of the total 100
points. The innovation with the highest score obtained 84.17 points, while the
lowestranked innovation only 14.17 points. When looking at the individual sub-indicators, one
can observe that MPI has the highest and the IMI has the lowest average value. The
average MPI score is 64.39 and the average IMP score is 35.67 points. The average score
of the IRI is 36.49 points.
Based on the presented evidence, it can be concluded that, on average, market potential
and innovation readiness are among the strongest dimensions of the innovations coming
out of the reviewed ICT FP7/CIP projects. In contrast, innovation management represents
the weakest dimension of these innovations.
Considering the type of organizations that are identified as ‘‘key organisation(s) in the
project delivering an innovation’’, Table 3 shows that, on average, there are 0.9 university
involved in an innovation developed within an FP7 project. In contrast, there are 0.45
SMEs per innovation and only 0.35 large companies per innovation. The involvement of
other types of organizations, e.g. public bodies, is even less significant. As indicated by the
values of standard deviation, there are considerable differences between innovations with
respect to the type of organizations involved in their development. Thus, there are cases
where only universities or only SMEs are indicated as the key organisations in delivering
It is worth noting that SMEs accounted in FP7 for 16% of total participations (2935 in
total) and 14% of total EC funding (€850 million in total)
their involvement as key organizations in delivering innovations in FP7 projects is
threefold higher than their participation rate. In comparison, High Education and Schools
and Research Centres account for 29% of the total number of organizations, but they
represent by far the most significant category of recipients in terms of funding (63%) and
large companies are the 29% of participating organisations and represent 20.5% of
Regarding the relationship between the organizational diversity and the potential of
innovations identified in EC-funded research projects, Fig. 1 presents average values of
scores of individual indicators across distinct types of innovation partnerships. The largest
average score of the IRI can be observed for innovation partnerships where at least two
SMEs work together, i.e. 45 points. With on average 38.6 points on the IRI, innovations
involving collaboration between universities and SMEs or between large companies rank
second in terms of innovation readiness. The same pattern can be observed for the
remaining indicators, except for the MPI, where innovations on which at least two large
companies collaborate achieve the highest average score, i.e. 71 points. This finding
reflects the higher market potential of innovations introduced by large companies. This
indicates that collaborations between homogenous organizations are more likely to deliver
innovations with higher potential for market commercialisation. This is particularly true for
the collaboration between SMEs. Innovations developed between this type of companies
are more likely to be technologically more mature. Moreover, the process of their
commercialisation is likely to be better managed, as compared to innovations introduced by
other types of collaboration arrangements. In other words, whenever an innovation is
introduced, SMEs collaborating together are more likely to solve such issues as the
question of innovation ownership, prepare business plan and market study or secure
investment from public or private sources.
6 Organizational diversity and innovation potential
In order to explain the dependency between the potential of innovations developed in ICT
FP7 projects and the type of partnership of organizations involved in their development, we
define our dependent variable yi as one of the previously specified indicators of innovation
potential, i.e. IRI, IMI, MPI and IPI (see Sect. 4.1). Among the independent variables there
are six dummy variables that control for the type of partnerships of organizations that were
identified by project reviewers as ‘‘key organisation(s) in the project delivering an
innovation’’ (see Sect. 8.1). Three of these variables control for the existence of homogenous
partnerships, i.e. University & University, SME & SME, Large & Large. In each case, there
are at least two organizations of the same type. The other three dummy variables control
for the existence of heterogeneous partnerships, i.e. University & SME, University & Large
and SME & Large. In this case, the dummy variables take value 1 when there are at least
two organizations belonging to different classes, e.g. one university and one SME, or SME
and one large company. In addition, to control for the size of partnership we include the
Number of key organizations variable, where the maximum is 3.
First and Interim review dummy variables control for the maturity of the project. Each
of them takes value 1 if the project is reviewed for the first or second time respectively and
0 otherwise. The reference group is in this case the final review of a project. Project
funding and duration control for the amount of funding and duration of a project.
Table 4 reports the results of OLS estimations. Regarding the test of IRI, i.e. innovation
readiness relating to the technical maturity of an evolving innovation, two variables
controlling for the type of partnerships are statistically significant. SME&SME variable has
a positive and SME&Large variable negative impact on the IRI score. In other words,
homogenous partnerships of among SMEs are more likely to positively influence the
technological maturity of an innovation. This involves such steps necessary to
commercialise new products or service as prototyping, demonstration or testing activities or a
feasibility study, and to secure the necessary technological resources, e.g. skills, to bring
the innovation to the market. In contrast, the involvement of an SME and a large company
in the development of new innovation is likely to slow down the process of technology
Relatively similar results are for the IMI that captures issues related to innovation
management. Here, again, we can see that SME&SME has a positive and the SME&Large
variable negative impact on the IMI score. However, the Large&Large variable has a
positive influence on the likelihood of undertaking such steps as, for example, clarifying
the related ownership and IPR issues, preparing a business plan or market study, securing
capital investment from public and/or private sources, or engaging an end-user in the
project. The results of the impact of the type of innovation partnership on the market
potential are inconclusive.
In the final estimation, i.e. the aggregated innovation potential indicator, we can see that
the existence of homogenous innovation partnerships of SMEs or large companies is
positively related with innovation potential measured by IPI. In contrast, heterogeneous
partnerships between SMEs and large companies seem to have a negative impact on the
innovation potential. Because none of the variables controlling for the involvement of
university as ‘‘a key organisation(s) in the project delivering an innovation’’ is statistically
significant, no conclusions can be made. However, in all cases the sign of the coefficient
controlling for the presence of a university in a partnership is negative.
University & University
Large & Large
University & SME
University & Large
SME & Large
Number of key organizations
Review time—reference point: final review
Prob [ F
Type and number of organizations identified as key organizations to bring the innovation to the market
The dependent variable is the score in individual innovation potential assessment criteria and the final
composite index of innovation potential, as defined in Sect. 4.1. The list of explanatory variables includes:
First, a set of variables on the type and number of organizations identified as key organizations to bring the
innovation to the market, i.e. where with the at most three key organizations to bring the innovation to the
market are such combinations as two universities (University & University), SMEs (SME & SME), large
companies (LARGE & LARGE) or at least one university and one SME (University & SME), or at least one
university and one large company (University & LARGE) or at least one SME and one large company (SME
& LARGE), and the number of key organizations to bring the innovation to the market (number of key
organizations). Second, information on the project review time, where the reference point is the final review.
Third, such project features as project funding and duration
All models report OLS regression estimates. Standard errors are reported in parentheses
*** Significant at the 1% level
** Significant at the 5% level
* Significant at the 10% level
Regarding the overall number of organizations involved in delivering an innovation in a
FP7 project, it can be seen that for all measures of innovation potential it has negative
impact. The same observation can be made with respect to the review time. As compared
to the final review, coefficients of dummies controlling for the first and interim review are
negative. In other words, we can say that the innovations mature and increase their
potential, as projects progress.
Concerning the remaining features of the project, we can say that, overall, neither
project funding nor duration has an impact on the measures of innovation potential.
Though very small, only the variable controlling for project funding has a positive impact
on the IMI and IPI.
The current paper uses data collected in a formal process of identifying and assessing the
potential of innovations developed within EC-funded projects. Knowing that project
consortia are characterised by a high level of organizational heterogeneity, we analyse the
question of what is the relationship between the organizational diversity and the innovation
potential. The current work differs from most of the existing research on RJV in two
aspects. First, it looks at the innovative output of research collaborations. Analysing
innovative rather than, for example, financial outcomes allows us to assess the
transformative effect of publically-funded research. Second, we look at the performance at the
project level, rather than at benefits of individual participants.
Our results show that the composition of innovation partnerships has an impact on the
innovation potential of innovations developed in publically-funded research projects. The
innovative potential of research output of homogenous partnerships, e.g. between two
SMEs or two large companies, is likely to be higher, as compared to heterogeneous
partnerships, e.g. an SME and a large company.
The above point is mainly visible in the context of innovation readiness and innovation
management. The concept of innovation readiness covers such issues as prototyping,
demonstration or testing activities or a feasibility study, and to secure the necessary
technological resources, e.g. skills, to bring the innovation to the market. Innovation
management refers to the capability of the project’s team to execute the necessary steps to
transform a novel technology or research results into a marketable product and to
commercialise it. Such steps may include, for example, clarifying the related ownership and
IPR issues, preparing a business plan or market study, securing capital investment from
public and/or private sources, or engaging an end-user in the project. Our results suggest
that that, due to, for example, coordination problems or differences in organizational
processes, organizations of the same type, e.g. two SMEs or two large organizations, are
more likely to find solutions to the problems that may arise when bringing an innovation to
Considering that we find that neither project funding nor duration affects the potential of
innovation, we conclude that the design of a consortium is more important than the level of
R&D input in explaining its innovative performance. In addition to technological and
product market proximity of members, reported previously as relevant for the outcomes of
(Branstetter and Sakakibara 2002)
, we can also add that the organizational
diversity of members plays a role for the positive outcomes of such collaborations.
We can conclude that differences in innovation performance of RJV result from various
capabilities, motivations and needs related to technology of different types of
organizations. For example, we show that innovations (co-)developed by SMEs exhibit high
commercialisation potential. This is consistent with the previous findings suggesting that,
when participating in publically-funded research projects, small companies have very
(Polt et al. 2008)
. Although large firms are also likely to deliver innovations
with high potential, it is unlikely that this will take place in collaboration with a smaller
partner or a university. They adopt a strategy focused on technology watch and active
acquisition of new knowledge from partners, rather than joint development and
commercialisation of a novel technology
(Hernan et al. 2003; R o¨ller et al. 2007)
. What is worth
noting is the fact that although universities alone are not particularly likely to introduce
ready to commercialize innovations
. Universities often report partnership
with other companies as the main need to bring their innovations to the market
. They also tend to report more needs related to the finalisation of the
innovation and the subsequent steps to bring it to the market, while private organisations
needs are more relate to the commercialization of the innovation and the need to create or
expand their market, i.e. scaling-up.
The results imply that organizations joining research consortia need to be aware of these
differences and find a way to ensure the right balance between technological and
technology commercialisation capabilities available in a consortium. Policy makers also need
to take into account these peculiarities. The process of designing support mechanisms for
technology commercialisation should account for the diversity of needs related to different
types of organizations and partnerships.
Our research has some limitations. First of all, it makes use of survey data, which
provides only very limited information about the actual content of the innovative output.
This, for example, does not allow us to quantify the economic value of innovations. Also
the framework used to assess the innovative potential reduces the richness of innovative
activity. It does not distinguish, for example, between radical and incremental innovations.
Finally, both the innovation questionnaire and the assessment framework focus on applied
and marketable outcomes of research projects. This naturally favours private organizations
and, hence, projects that are dominated by firms.
Regarding further extension of the current work, we believe that it would benefit from
extensions that account for, example, for technological relatedness of participating
organizations. Also controlling for the geographic origin of the participants would allow to cast
some light on the role of physical proximity and coordination cost in the working of
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Innovation potential assessment framework
No consortium internal IPR issues that could compromise the ability of a project
partner to exploit the innovation
Company’s business unit involved in project activitiesb
End-user in the consortium
No end-user in the consortium or consulted
Commitment of relevant partners to exploit innovation
a GQ general questions
b Steps DONE in the project in order to bring the innovation to the market
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