RESEARCH ARTICLE

Proposal success in Horizon 2020: A study of the
influence of consortium characteristics

Iris Wanzenböck1

, Rafael Lata2

, and Doga Ince2

1Copernicus Institute of Sustainable Development, Utrecht University, Princetonlaan 8a, 3584 CS Utrecht, Die Niederlande
2Austrian Research Promotion Agency FFG, Sensengasse 1, 1090 Vienna, Österreich

Schlüsselwörter: consortium level, EU Framework Programme, Horizon 2020, R&D collaboration,
research funding, research policy

ABSTRAKT

This study draws on evaluation data to investigate the success of collaborative R&D project
proposals submitted to Horizon 2020, the European Union’s Framework Programme for
Research and Innovation (FP). Data on project status and evaluation score are used to identify
successful and rejected project proposals. We hypothesize that the social or institutional
composition of the project consortium explains the outcome of an early-stage R&D collabo-
ration. Using regression analysis, we identify “success factors” at the consortium level, related
Zu (A) the network visibility; (B) level of experience and degree of acquaintance; Und (C) Die
research capabilities and excellence or reputation of consortium members. We show that
consortia with high levels of experience and reputation, involving a large share of Western
European partners and engaged in more application-oriented consortia, have greater chances
of success in acquiring H2020 project funding. This result has implications for the scientific
Gemeinschaft, as well as for the direction of EU research policy.

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1.

EINFÜHRUNG

Forschung, Technologie, and innovation (RTI) policy is increasingly under pressure to legitimize the
provision of public subsidies and demonstrate the impact they generate on the research and inno-
vation system and society as a whole. Demand for efficient and well-performing research funding
systems has also spurred scientific interest in the study of the funding procedures, selection out-
comes, and additionality effects of publicly funded research grants and partnerships (sehen, for exam-
Bitte, Bornmann, Leydesdorff, & Van den Besselaar, 2010; Luukkonen, 2000; Viner, Powell, & Grün,
2004). A prominent example in the scientific literature is the research and development (R&D) Profi-
jects funded by the Framework Programmes for Research and Innovation of the European Union (EU
FP) (Balland, Boschma, & Ravet, 2019; Breschi, Cassi, et al., 2009; Defazio, Lockett, & Wright, 2009;
Protogerou, Caloghirou, & Siokas, 2010). Scholars have investigated the organization-specific
determinants of EU-wide R&D collaborations (Autant-Bernard, Billand, et al., 2007; Lepori,
Veglio, et al., 2015; Paier & Scherngell, 2011), the geographical composition and evolution of these
partnerships (Balland, 2012; Balland et al., 2019; Chessa, Morescalchi, et al., 2013; Scherngell &
Barber, 2009; Scherngell & Lata, 2013), and their actual impact on the research system in different
European regions (Hoekman, Scherngell, et al., 2013; Wanzenböck & Piribauer, 2018).

Most EU FP studies only investigate awarded projects: R&D collaborations that successfully
passed the proposal stage to enter an operational phase. A central finding of these studies is the
oligarchic network structure created by the funding program (Breschi & Cusmano, 2004),

Keine offenen Zugänge

Tagebuch

Zitat: Wanzenböck, ICH., Lata, R., &
Ince, D. (2020). Proposal success in
Horizon 2020: A study of the influence
of consortium characteristics.
Quantitative Science Studies, 1(3),
1136–1158. https://doi.org/10.1162/
qss_a_00067

DOI:
https://doi.org/10.1162/qss_a_00067

Erhalten: 29 Oktober 2019
Akzeptiert: 30 April 2020

Korrespondierender Autor:
Iris Wanzenböck
i.wanzenbock@uu.nl

Handling-Editor:
Ludo Waltman

Urheberrechte ©: © 2020 Iris Wanzenböck,
Rafael Lata, and Doga Ince. Published
under a Creative Commons Attribution
4.0 International (CC BY 4.0) Lizenz.

Die MIT-Presse

Proposal success in Horizon 2020

characterized by a small core of research-intensive organizations concentrated in Western Europe
which participate in many projects and thus take a lion’s share of the project funds (Balland et al.,
2019; Protogerou et al., 2010; Wanzenböck, Scherngell, & Lata, 2015). Im Gegensatz, Organisationen
positioned at the network periphery are rarely involved in projects and are only loosely connected to
the core players. The persistence of this finding across several EU FP additions has fed scientific and
political debates about the goals and funding criteria, particularly whether the EU FPs should serve as
excellence programs designed primarily to fund the very best researchers, proposals, and project
teams across Europe, or whether they should also aim to widen the European research landscape
(Caloghirou, Tsakanikas, & Vonortas, 2001; Hoekman et al., 2013; Makkonen & Mitze, 2016).

The composition of the project teams also raises questions of additionality; namely whether
the collaborative funding mechanisms are actually able to create new partnerships, or if they
simply reproduce patterns already firmly established in the European research landscape.
Jedoch, investigations on only awarded collaborations cannot deliver sufficient insight into
the types of organization or project consortia that are most likely to secure funding. Mostly
due to data limitations, empirical studies on rejected project applications are highly underrep-
resented in the literature (Bornmann et al., 2010). For the EU FPs, Enger and Castellacci (2016)
and Enger (2018) are important exceptions, investigating the participation of universities in both
funded and nonfunded Horizon 2020 (H2020) project applications. Jedoch, these studies
consider only a specific country (Enger & Castellacci, 2016), or type of organization (Enger,
2018), and are thus limited in scope. They can provide only partial insights into how the publicly
funded R&D network structures spanning Europe came into being.

In this study we focus on project consortia comprising different types of organizations from
different European countries to systematically analyze which types of project teams are more
successful in receiving EU H2020 funds for collaborative R&D. By viewing R&D collaboration
as a dynamic process (Kumar & Nti, 1998; Majchrzak, Jarvenpaa, & Bagherzadeh, 2014), Wir
assume that the social and institutional composition of the project consortium is crucial for
partner-specific learning, particularly in the early collaboration phase, and therefore consortium-
specific factors have a significant influence on the chances of success of collaborative project
applications. Based on arguments from social network theory and previous literature on EU FPs,
we theoretically identify and empirically test a specific set of success factors related to (A) Die
prominence and visibility of the consortium in the H2020 network; (B) the experience of its
members with previous FP projects and the degree of acquaintance among them; Und (C) Die
research capabilities and academic excellence or reputation of the members.

Our empirical investigation draws on proposal evaluation data as included in the eCORDA
database. We restrict our sample to project proposals submitted to the H2020 Societal
Challenges pillar, resulting in a final sample of around 23,000 organizations located across
Europe collaborating in about 7,000 consortia (Projekte) during the period June 2014 to June
2016. To systematically assess how consortium characteristics relate to a distinct proposal
outcome, we rely on different types of regression models and outcome variables: Erste, wir gebrauchen
data on the status of the project proposal (main list, above threshold, below threshold) in einem
multinomial logistic regression (MLR) Rahmen, und zweitens, we make use of proposal eval-
uation scores in an ordinary least squares (OLS) regression model.

Our study is an important addition to the existing literature on publicly funded R&D, in par-
ticular EU FPs, for several reasons: Erste, we draw on a unique and comprehensive data set
including all rejected H2020 project proposals to differentiate between successful project appli-
Kationen, as typically identified in the literature, and unsuccessful ones. Zweite, with the focus
on the project consortia rather than specific organizations, we provide novel evidence of the

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Proposal success in Horizon 2020

proposal success factors as they relate to entire project teams participating in H2020. Dritte, unser
empirical results are an important contribution to better dissecting the H2020 network structures,
the underlying funding patterns, and associated knowledge diffusion structures, as requested by
the literature on EU FPs for some time.

The remainder of the paper is structured as follows: Abschnitt 2 discusses the importance of
consortium-specific characteristics in early-stage collaborations, from which our hypotheses
about distinct social and institutional factors and their influence on the success of proposals
are derived. Abschnitt 3 introduces the scale and scope of the H2020 database and our proposal
success measures, before Section 4 explains the empirical modeling strategy. Abschnitt 5 presents
the regression results, and Section 6 concludes with a synthesis and some policy implications
derived from our empirical results.

2. EARLY-STAGE COLLABORATIONS AND THE IMPORTANCE OF
CONSORTIUM-SPECIFIC FACTORS

Project-based collaborative R&D funds are a key element in most public research funding portfo-
lios, and are designed to bring together researchers from different research traditions, institutional
backgrounds, and locations. Temporary partnerships in the form of short- to medium-term projects
should enable researchers to better meet the challenges posed by the increasing complexity of
knowledge and the need to integrate a diversity of technologies, as well as the growing demand
for solutions of immediate relevance to society (Ahuja, 2000; Cummings & Kiesler, 2005; Wuchty,
Jones & Uzzi, 2007).

Typically, project-based R&D alliances follow an evolutionary process that can be charac-
terized by different stages and collaboration dynamics (Kumar & Nti, 1998; Majchrzak et al.,
2014). Anfänglich, interorganizational collaborations start by forming a consortium to jointly de-
velop a research agenda, followed by an operational stage in which the collaboration agreement
or research plan is translated into reality. A central finding of studies in innovation and collab-
oration management is the changing modes and intensities of interaction throughout the collab-
oration process, characterized by different patterns of learning between partners over time (Das
& Kumar, 2007; Majchrzak et al., 2014; Thune & Gulbrandsen, 2014). It is assumed that content-
related factors, such as cognitive proximities, the absorptive capacities, and the ability to
combine knowledge across partners (content learning), are crucial for the operational stage of
a collaboration, while at the start of a project, social and institutional factors appear more
important because they initially shape the cooperation and condition its continuation (Thune
& Gulbrandsen, 2014). Partners need time to develop a mutual understanding of a phenomenon,
to identify complementarities, and to balance organization-specific interests dedicated to the
research plan. “Partner-specific learning” (Das & Kumar, 2007) enables the development of
mutual trust, planning for the project’s future stages and the management of the partners’ related
expectations. For long-distance or cross-sectoral partnerships in particular, the first stage may be
complex and time-consuming (Torre, 2008). Jedoch, the better the partners manage the early
stage, the more effective and successful the outcome of the collaboration.

Focusing on consortium-specific social or institutional factors would seem to be an appropri-
ate anchor point for the investigation of publicly funded R&D collaborations in their proposal
Phase. Hier, the early collaboration stage encompasses the search for partners and complemen-
tarities on the input side (d.h., Wissen, skills, interest), the development of a joint research
agenda (d.h., the proposal), and task coordination and resource division within the consortium
(d.h., financial and time), which eventually leads to the joint project application. The better the
cooperation during this early phase, the higher the potential quality of the proposal and the

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Proposal success in Horizon 2020

chances of success in acquiring the research funds. Folglich, the probability of receiving
funding may not only be determined by the content, thematic orientation, or skill level and
expertise match of the individual organizations. Stattdessen, the interplay of social or institutional
factors is assumed to facilitate or hamper effective interaction and partner-specific learning.

2.1. Consortium-Specific Factors: Main Assumptions

In this study we investigate whether distinct partner configurations are more likely to lead to
proposal success. We define a consortium associated with a proposal as a temporary alliance
between organizations, engaging in early-stage collaboration to jointly apply for research funds
to later conduct collaborative research within the boundaries of a specific project. Based on pre-
vious studies on publicly funded research in general (Abbasi, Hossain, & Leydesdorff, 2012;
Bornmann et al., 2010; Viner et al., 2004) and collaborative research and networks within EU
FPs in particular (Enger, 2018; Lepori et al., 2015; Paier & Scherngell, 2011), we derive a set of
potential consortium-specific success factors for further investigation in our empirical analysis.

Network visibility and centrality

Collaborative research is increasingly viewed in terms of a network system. Given the participation
in different R&D projects, research performing actors or organizations are directly and indirectly
connected in a network (Breschi & Lissoni, 2004; Newman, 2001; Tomasello, Napoletano, et al.,
2016). An actor’s embeddedness in a global network structure is supposed to influence the deci-
sion with whom to engage in collaboration, the quality of knowledge flows and the value arising
from the collaboration (Burt, 2005; Gilsing, Nooteboom, et al., 2008; Granovetter, 1973). Es ist
assumed that highly connected actors have a comparative advantage over others, as they are more
deeply embedded, more exposed to novel knowledge, and more likely to receive strategically
valuable information (Owen-Smith & Powell, 2004; Tsai, 2001; Wanzenböck et al., 2015).

We argue that, at the start of a joint research endeavor, the advantages of great network
visibility become manifest in the search for synergies between an actor’s own competences and
the expertise of others. Network visibility facilitates the formation of multidisciplinary or multi-
institutional partnerships, and facilitates contact to strategically important partners such as star
scientists or organizations, which can boost the likelihood of receiving public funding. Darüber hinaus,
centrally positioned partners can more easily seek the tacit knowledge needed to align with the
criteria of the research call, and to design a more targeted proposal accordingly. Such network
Effekte, often referred to as preferential attachment or accumulative advantage, can explain the
emergence of a Matthew effect, as often observed for publicly subsidized research (Bornmann
et al., 2010; Enger, 2018; Viner et al., 2004). We therefore propose a positive relationship between
network centrality and proposal success and test the following hypothesis empirically:

Hypothesis 1: The greater the network visibility of a project consortium, the more successful
its project proposal.

Acquaintance and experience of partners

In addition to external networks beyond the consortium, the social relations between consortium
partners and a consortium’s internal cohesion may determine the quality and success of a research
proposal. When applying for collaborative research funds, two factors seem to be particularly im-
portant: (A) the level of acquaintance among the consortium partners, Und (B) the level of mana-
gerial experience in coordinating project partners within the consortium. The former relates to the
idea of “strong ties” between consortium members who are closely connected and enjoy a certain

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Proposal success in Horizon 2020

level of mutual trust (Granovetter, 1973; Krackhardt, 2003). Such strong relationships are usually
the result of repeated interactions over time. A shared history allows collaboration partners to draw
on partner-specific experiences, established routines, and interaction modes, making it easier for
the consortium to tackle the challenges of the early stage (Das & Kumar, 2007). For multidisciplin-
ary research teams in particular, previous relationships may be important in bridging differences in
perspective caused by dissimilar work cultures, scientific norms or typical approaches to problem
solving, to strengthening the commitment to the joint project and team cohesion, and to develop-
ing a shared research vision (Ratcheva, 2009). Knowledge about the partners’ expertise and skills
can contribute to a more efficient division of tasks among partners and facilitates the development
of a coherent thematic orientation.

A second factor related to experience is the ability to effectively manage a team of diverse project
Partner. Typically, the management responsibility for a joint R&D project lies in the hands of a
single project coordinator who plays a central role in handling the information exchange and
knowledge flows between the partners (Enger & Castellacci, 2016; Maggioni, Uberti, & Nosvelli,
2014). In large-scale and complex project constellations, management capabilities are essential to
select the right project partners, to establish effective communication structures, and to align the
activities and expectations with respect to the project aims. We therefore argue that coordination
experience contributes to successful project acquisition due to learning effects (such as those related
to the specifics of the funding scheme or the qualities of partners) that facilitate the development of
a coherent project proposal. Entsprechend, we will test the following hypothesis empirically:

Hypothesis 2: The higher the acquaintance and experience of the partners in a project
consortium, the more successful the project proposal.

Research capabilities and excellence

Empirical studies suggest that both the research capabilities and scientific excellence of organi-
zations have an influence on FP participation and the likelihood of receiving funding (Enger,
2018; European Commission, 2017; Lepori et al., 2015). Researchers and organizations with
a good past performance and an established knowledge base in the field are likely to be more
productive, generate higher impact research, and be more likely to acquire funding for their
research idea (Van den Besselaar & Leydesdorff 2009). Darüber hinaus, organizations with an estab-
lished scientific or research reputation can typically select the partners they would prefer to
engage with in a joint project. The favorable opportunities for selection may create a tendency
towards collaborating with partners of similarly high quality (a phenomenon typically referred to
as homophily in social network theory; McPherson, Smith-Lovin, & Cook, 2001), and a bias
towards participating only in the most promising consortia or proposals.

Given that a project proposal in the EU FP depends on the quality and research profile of
the entire consortium, we can assume that project consortia involving a large number of high-
quality and high-impact organizations will achieve a higher evaluation score for their project.
We therefore propose the following hypothesis:

Hypothesis 3: The higher the research capabilities and excellence of a project consor-
tium, the more successful its project proposal.

3. PROPOSAL SUCCESS IN H2020: CONTEXT AND DATA

With the support of precompetitive, collaborative R&D, the overall aim of the EU FPs has been
to strengthen the scientific and technological base of the European scientific community and

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Proposal success in Horizon 2020

economy (Barker & Cameron, 2004; Delanghe, Muldur, & Soete, 2009). Even though the
fundamental rationale has remained unchanged since its inception in 1984, the most recent
FP—H2020—specifically promotes research and innovation on the basis of three objectives:
scientific excellence, industrial leadership, and societal challenges, as institutionalized in the
three program pillars of H2020 (European Commission, 2011). This study focuses solely on
collaborative R&D projects funded under the Societal Challenges pillar, a thematic H2020
framework incorporating seven predefined themes1. Over the entire funding period, aus
2014 Zu 2020, the total budget for H2020 is A77.2 billion; bei 37%, funding for Societal
Challenges accounts for the largest share of the total budget.

For this study, we are drawing on the H2020 eCORDA proposal database (publication date:
September 2016) containing largely harmonized applicant data2 and project data for all evalu-
ated projects (funded and not funded) that apply for H2020 funds. To analyze proposal success
in H2020, we restrict the empirical sample as follows: Erste, to ensure the consistency and com-
parability of our observations, we extract only projects funded under the Societal Challenges
pillar between 2014 Und 2016. Zweite, we focus only on projects involving instruments that
finance actual R&D (d.h., innovation actions, research and innovation actions), and exclude
funded coordination and networking activities (so-called Coordination and Support Action
(CSA), such as policy dialogs or training). Dritte, to exclude outliers, we consider only projects
with a consortium size of more than three and fewer than 16 Partner. Our final sample consists
von 7,208 project proposals (d.h., consortia). We consider around 69,357 participations by around
22,612 distinct organizations, based on which we construct the H2020 Societal Challenges pil-
lar network. The majority of these participations come from the private (for profit) sector (36%),
followed by research organizations (19%) and universities (36%). Public bodies account for 5%
and others for 4%.

3.1. Measuring Proposal Success

Each H2020 proposal subjected to the evaluation procedure receives an overall score based on
an assessment by experts. This expert score includes individual assessments in the categories of
impact, excellence, and implementation, and typically ranges from 0 Zu 153. According to the
score achieved, the project proposals are ranked and assigned a certain status. Three different
project statuses can be distinguished: (A) below threshold: a project proposal is assessed below a
certain threshold and rejected; (B) above threshold: a project is assessed above threshold but
rejected; Und (C) main list: a project is included in the main list and selected for funding. Der
threshold scores are predefined and may differ for each H2020 Societal Challenges program.
Data on both project status and evaluation score are contained in the eCORDA database, Und
will be used later as dependent variables in our empirical model variants.

1 The seven societal challenges are: (A) Health, demographic change, and well-being (hereafter referred to as
“Health”); (B) Food security, sustainable agriculture and forestry, marine and maritime and inland water
Forschung, and the bioeconomy (“Food”); (C) Secure, clean, and efficient energy (“Energy”); (D) Smart, Grün,
and integrated transport (“Transport”); (e) Climate action, Umfeld, resource efficiency, and raw mate-
Rial (“Environment”); (F) Europe in a changing world—inclusive, innovative, and reflective societies
(“Society”); Und (G) Secure societies—protecting freedom and security of Europe and its citizens (“Security”).
2 We perform consistency checks and data cleaning for the assignment to organization types and location

(country) of the project applicants.

3 Experts evaluate each criterion on a scale of 0 Zu 5; 0 points are given when the proposal fails to address the
criterion or cannot be assessed due to missing/incomplete information and the maximum number of 5 points
is given for a proposal which addresses all relevant aspects of the criterion and any shortcomings are minor
(see European Commission, 2019).

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Proposal success in Horizon 2020

Tisch 1. Descriptive statistics of the project status after proposal assessment, divided by program

Programm
Energy

Umfeld

Food

Health

Sicherheit

Society

Transport

Total

Below threshold/
rejected (A)
867

198

148

1,797

338

617

220

4,185

In % (A)

67.21

45.62

45.96

71.71

50.90

44.71

35.95

58.06

Above threshold/
rejected (B)
255

177

117

505

274

692

230

2,250

In % (B)

19.77

40.78

36.34

20.15

41.27

50.14

37.58

31.22

Main
list (C)
168

59

57

204

52

71

162

773

In % (C)

13.02

13.59

17.70

8.14

7.83

5.14

26.47

10.72

Total number
of proposals
1,290

434

322

2,506

664

1,380

612

7,208

In %

17.90

6.02

4.47

34.77

9.21

19.15

8.49

100

Tisch 1 shows descriptive statistics on the outcome of the proposal assessment (d.h., Projekt
Status) and its distribution across the seven H2020 programs. We see that only around 11% von
the project applications were successful, while 31% were evaluated as being above threshold
but not considered for funding, Und 58% were below threshold and immediately rejected.
Most of the proposals were submitted to the Health program, with the number of submissions
almost double the quantity of those submitted to the second largest program (Society). Der
Food program records the smallest number of submitted proposals. The Society program shows
the lowest success rate, with only around 5% being assigned to the main list. The success rate
was highest for the Transport program, with more than a quarter of all submitted proposals
receiving H2020 funding.

Darüber hinaus, we can compare the structure of the interorganizational H2020 networks based
An (A) only the successful (main list) project proposals, Und (B) alle (successful and nonsuccessful)
proposals submitted to the Societal Challenges pillar. Tisch 2 shows that we can observe only
minor differences in the global network structure and connectivity of organizations in the two
Netzwerke. Figur 1 underlines this finding, showing that the main network component is densely
knitted for both networks. It should be noted that the construction of both interorganizational
networks is based on a two-mode network data structure, in which the organizations are linked
by joint membership in H2020 project applications. This means that all project partners are
interlinked, even though it is likely that not all of the partners know each other equally well,
particularly during the proposal phase or in large consortia (Breschi & Cusmano, 2004). Das
assumption of fully connected project consortia can at least partly explain the high number of
organizations in the main network component and the structural similarities between the two
Netzwerke. With respect to organization type, we see that high centrality actors are mostly uni-
versities and research organizations that are located at the core of the network and surrounded
by a second “ring” consisting mostly of private for-profit organizations, such as industrial com-
panies or consultants. In the main component of the successful proposal network, we observe a
few densely connected “cliques” with high internal connection but only loose relations to other
actors, while such a clique structure cannot be observed in the full network.

A closer look at the participation intensity of the three major organization types in the
H2020 Societal Challenges pillar is seen in Table 3, which shows that private commercials
(mostly companies) contributed to project proposals almost as often as higher education

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Tisch 2. Descriptive network statistics of the interorganizational H2020 networks

Number of organizations

Number of projects

Mean degree

Max degree

SD degree

Share of main component

NEIN. of components

Centralization

Transitivity

Density

Average distance

Diameter

Network of successful
H2020 proposals
4,310

Network of all
H2020 proposals
22,612

773

18.96

608

23.70

0.99

5

0.14

0.58

0.004

3.12

6

7,208

25.53

3,549

71.46

0.99

27

0.15

0.81

0.001

2.94

7

Notiz: The network of successful proposals refers to projects with project status “main list” (C); the network of all
proposals includes all projects.

Organisationen (mostly universities), whereas the participation rate for research organizations is
somewhat lower. We also see that the higher education sector and the private sector requested
similar shares of funding, pointing to a fairly balanced participation of the two major sectors
across all the proposals (successful and nonsuccessful).

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Figur 1. The interorganizational H2020 network: successful vs. all proposals.
Notiz: The left plot shows the main component of a network constructed on the basis of successful
H2020 proposals aggregated for the seven Societal Challenges programs. The right plot shows the
main network component based on the full sample (d.h., successful and nonsuccessful H2020 pro-
posals submitted under Societal Challenges). For illustrative purposes, only links with weight > 1 Sind
displayed; the node size corresponds to degree of centrality.

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Tisch 3. Participation by organization type

Organization type
Higher education

Private (for profit)

Research organization

Participations
in proposals,
(all proposals)
6,772

Average participation
per proposal,
(all proposals)
3.72

Share of requested
funding (In %,
all proposals)*
37.9

6,538

5,794

3.77

2.26

35.5

20.2

* Organization types falling under the categories “Public” and “Others” were omitted from the analysis.

4. EXPLAINING PROPOSAL SUCCESS: EMPIRICAL MODEL SPECIFICATIONS AND VARIABLES

To gain insights into the factors distinguishing successful from nonsuccessful H2020 proposals,
we investigate a set of consortium factors and run different regression models on the proposal
assessment outcome. The first model is an MLR model (Long & Freese, 2014) basierend auf
three categories of project status: m = 1 for below threshold, m = 2 for above threshold,
and m = 3 for main list. We consider the status “below threshold” (m = 1) as our reference
category and specify the model in terms of
(cid:1)
exp xβ
mj1
(cid:1)
j¼3
j¼1 exp xβ

for m; j ¼ 1 Zu 3

P yc ¼ mjx
D

Þ ¼

(1)

P

jj1

(cid:3)

(cid:3)

to estimate the probability of observing a distinct status yc = m for a given set of consortium
factors x. The βs denote the associated coefficients for the comparison of category m, j to
category 1. The observations in x relate to measures of network visibility, acquaintance,
and experience of partners, and the research capabilities and excellence of a consortium as
discussed below. We rely on marginal effect interpretations to compare and interpret the
effects of our consortium variables across different model specifications (Long & Freese, 2014).

Außerdem, we estimate a linear regression model based on the actual evaluation scores

of the form

ys ¼ xβ þ (cid:2)

(2)

to relate the observed expert score ( ys) to the same set of consortium factors x as in Eq. 1. Hier,
the βs denote the associated coefficients as derived from an OLS regression, Und (cid:2) the i.i.d. Fehler
Begriff. The main descriptive statistics of the variables are shown in Table A1 in the Appendix.

The explanatory variables in x reflect (A) network visibility, (B) experience and acquaintance,
Und (C) the research capabilities and excellence of a project consortium as discussed in Section 2.
Network visibility is defined as the network centrality of the consortium, measured in terms of the
sum of the centralities of the individual project partners as calculated on the basis of our full H2020
network for all project proposals in Societal Challenges. Network centrality is considered in terms
of degree and eigenvector centrality (Bonacich, 1987; Wasserman & Faust, 1994)4. Because net-
work centrality measures are typically highly correlated ( Valente et al., 2008), we consider them
separately and estimate different model variants.

4 Zusätzlich, we calculated betweenness centrality to check the robustness of our model specifications. Der
results did not change significantly and are available upon request. All centrality measures are calculated
with the package igraph in R.

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Experience and acquaintance of partners in the consortium is measured by two variables: Erste,
coordination experience is a dummy variable and takes the value of 1 if the project coordinator has
already coordinated a project funded by the 7th Framework Programme (2007–2013), Und 0
ansonsten. Zweite, acquaintance is a count variable related to the sum of joint participations of
Organisationen (d.h., project partners) in FP7 projects. To measure such prior FP collaborations at
the level of organizations, we match the organizations in our sample with the FP7 project and ap-
plicant data as contained in the FP7 eCORDA database5.

To reflect the research capabilities and excellence of the consortium, we consider both the
application orientation (technological development) and scientific excellence of the project team.
The first variable (patents) is measured by the number of patents of the consortium (d.h., all project
Partner) in societal challenge-relevant technologies for the period 2007 Zu 2015. For this purpose,
we match the organization names in the H2020 eCORDA database with the information on patent
applicants as included in the OECD REGPAT database. Patents relevant to Societal Challenges
were selected on the basis of the IPC classification suggested by Frietsch, Neuhäusler, et al.
(2016). We use fractional counting to construct the variable. Scientific excellence is a count var-
iable reflecting the number of project partners belonging to one of the top 50 universities according
to their publication impact as listed in the CWTS Leiden Ranking 2017 (Centre for Science and
Technology Studies, 2017). The ranking is based on the number of a university’s publications
belonging in the top 1% of most frequently cited publications in their field6. Als solche, scientific
excellence is considered as a proxy for a university’s reputation, assuming that especially elite
or prestigious universities can be more selective in their choice of proposal or partner.

Next to this core set of variables, we include a number of control variables, which reflect
more general consortium member or project-related characteristics. These include the institu-
tional composition of partners, measured as the share of consortium partners belonging to the
major sectors; that is higher education organizations (mostly universities) (HES ); private com-
mercials (mostly companies, VR China ); or research organizations (REC ). Außerdem, we construct
a CEE country variable, which reflects the number of consortium members from Central and
Eastern European Countries (EU-13).

Zusätzlich, we include a dummy variable to indicate whether the project is part of a multi-
lateral public-private partnership (PPP) initiative. PPP initiatives7 develop strategic plans mostly
with the aim of strengthening European economic competitiveness, and should be implemented
through specific calls under the H2020 program. Project consortia based on such PPPs are highly
experienced, trust based, and thematically focused, and can have a considerable influence on

5 Note that we measure joint participations at the level of organizations. A lower level of analysis, wie zum Beispiel
participations of distinct institutes or departments of large organizations, would be desirable but is not pos-
sible, as the FP7-eCORDA database does not contain institute- or department-specific information.

6 We ran several robustness checks for our scientific excellence variable, including impact-based CWTS rank-
ings, lower thresholds for the top universities (from top 50 to top 20), or the mean impact score of the top
universities in a consortium. All results are robust, and are available upon request.

7 PPPs can be categorized into two types: PPPs of contractual nature and PPPs of institutional nature. The PPPs
of contractual nature are solely based on contractual links between industrial companies and the public
sector (represented by the European Commission). The aim of these PPPs is the development of innovative
technologies in European key industries, those that were particularly strongly affected by the financial and
economic crisis. The calls in the contractual PPPs are implemented exclusively through the H2020 program.
Im Gegensatz, PPPs of institutional nature (d.h., Joint Technology Initiatives—JTI) are long-term public-private
partnerships supporting transnational research collaboration in selected fields of technology. The coopera-
tion between the public and private sector takes place within a distinct entity with the aim of increasing the
competitiveness of European industry in selected technology sectors. The research funding is provided by
industry and the public sector and implemented through calls for tender.

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agenda setting in the H2020 program lines. Our variable partnership initiative takes the value 1 Wenn
the project is part of such a PPP initiative, Und 0 ansonsten. To control for size effects, Wir
compute the variables consortium size, which is the number of project proposal members,
and project size, which measures the amount of funding requested in the project proposal.
Darüber hinaus, we include dummy variables for each thematic program in the H2020 Societal
Challenges pillar to control for potential heterogeneities in our estimations due to broader pro-
ject themes and research field-specific characteristics. An overview of all the variables is given in
Tisch 4.

5. EMPIRICAL RESULTS

Tisch 5 presents the estimation results for our proposal success model based on the MLR
model as specified in Eq. 1. Due to the high negative correlation between the share of private
firms (VR China) and share of universities (HES) in a consortium (see Table A2 in the Appendix), Wir
were not able to estimate a model including both variables. We therefore estimated two model
variants; the first four columns contain the results for the model specification with HES shares,
and the last four columns the results for the model with PRC shares. We also estimated differ-
ent model variants for degree centrality (Models 1 Und 3) and eigenvector centrality (Models 2
Und 4). Associated marginal effect calculations are reported in the Appendix (Table A3). Der
model variants can be interpreted separately as if they were simple logit models. The project
status “below threshold” (category 1) is the reference for interpreting the coefficients for all
MLR model specifications. Tisch 6 shows the OLS estimation results of the model based on
the expert scores as specified in Eq. 2.

Turning now to the estimation results, we observe a positive relationship between the net-
work visibility of a consortium and the evaluation outcome of its H2020 proposal for the expert
score models (Tisch 6) and all “above threshold” model variants (Tisch 5). Interessant, im
“main list” variants the coefficients for degree centrality are insignificant, but slightly signifi-
cant and positive for eigenvector centrality for the HES model (Modell 2). The marginal effects
as illustrated in Figure 2 for different levels of degree and eigenvector centrality further explain
diese Ergebnisse. We see that, while keeping all other variables constant at their mean values, Die
effect on proposal success does not increase for degree centrality, but increases slightly with
higher levels of eigenvector centrality. This result suggests that being more directly connected
to core players can indeed increase the likelihood of succeeding in the application for H2020
funding. Our Hypothesis 1 stating a positive relationship between network visibility and pro-
posal success is therefore only partly confirmed by the empirical results.

It is noteworthy that our findings are only partly in line with the Enger (2018) Studie, welche,
although it only looks at highly connected universities, points to an unambiguously higher
likelihood of proposal success. If we look at the network visibility of entire consortia, es scheint
that high network embeddedness does indeed positively contribute to submitting a strong pro-
posal (above threshold). Jedoch, highly connected consortia are not necessarily more likely
to get funded in H2020 (main list) than consortia with a lower centrality. The findings for
eigenvector centrality may, at least partly, explain the closed and oligarchic network structure
of FP networks revealed in the studies for funded collaborations (z.B., Autant-Bernard et al.,
2007; Breschi & Cusmano, 2004).

Regarding our second hypothesis, we observe that coordination experience in the prede-
cessor program (FP7) is a good predictor of proposal success in H2020, the follow-up edition.
The likelihood of achieving “main list” or “above threshold” project status is significantly

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Variable
Dependent variables

Tisch 4. Overview and description of variables

Definition

Quelle

Project status

Categorical variable according to project status: main list, above threshold,

H2020 eCORDA database

below threshold

Expert score

Score based on project evaluation in the categories impact, excellence and

H2020 eCORDA database

implementation (range from 0 Zu 15)

Consortium factors

Degree

Sum of the degree centralities of the consortium partners (log), based on

H2020 eCORDA database

full H2020 Societal Challenge network

Eigenvector

Sum of the degree centralities of the consortium partners (log), based on

H2020 eCORDA database

full H2020 Societal Challenge network

Coordination
Erfahrung

Dummy variable (1 if project coordinator coordinated a project funded

FP7 eCORDA database

by FP7, 0 ansonsten)

Acquaintance

Number of joint participations of project partners in FP7 projects (zählen)

FP7 eCORDA database

Partnership initiative

Dummy variable (1 if project is part of a multilateral public-private

H2020 eCORDA database

Partnerschaft (PPP) initiative, 0 ansonsten)

Scientific excellence

Number of consortium partners belonging to top 50 universities. Ranking
based on the number of publications in the 1% of most frequently cited
publications in their field (zählen)

CWTS Leiden Ranking

Patents

Number of consortium patents in technologies relevant to Societal

OECD REGPAT

Challenges, 2007 Zu 2015. Classification based on IPC classes according
to Frietsch et al. (2016), fractional counting

REC

VR China

HES

Number of consortium partners assigned to the research organization

H2020 eCORDA database

(REC) sector, as a share of total number of partners

Number of consortium partners assigned to the private commercial

H2020 eCORDA database

(VR China) sector, as a share of total number of partners

Number of consortium partners assigned to the higher education service

H2020 eCORDA database

(HES) sector, as a share of total number of partners

CEE countries

Number of consortium partners from Central and Eastern European

H2020 eCORDA database

Countries; EU-13 (zählen)

Controls

Thematic program

Dummy variable for thematic programs in the H2020 Societal Challenges

H2020 eCORDA database

pillar (TPT, Energy, Umfeld, Food, Health, Sicherheit, Society
[reference category])

Project size

The amount of funding requested in the project proposal

H2020 eCORDA database

Consortium size

Number of consortium (project proposal) members

H2020 eCORDA database

higher for consortia with an experienced coordinator compared to coordinators without FP7
Erfahrung. Jedoch, wie in der Abbildung gezeigt 3 (left plot), we do not observe significant differ-
ences between the effects of coordination experience on being ranked on the main list and
above threshold.

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Tisch 5. Results of the MLR models

(1)

(2)

(3)

(4)

Degree

0.096 (0.071)

Main list

Above threshold/
rejected
0.262*** (0.051)

Main list
–

Above threshold/
rejected
–

Main list

0.045 (0.069)

Above threshold/
rejected
0.263*** (0.050)

Main list
–

Above threshold/
rejected
–

Eigenvector

Coord.

Erfahrung

–

–

0.168** (0.075)

0.362*** (0.056)

–

–

0.094 (0.071)

0.352*** (0.054)

0.336*** (0.086)

0.252*** (0.059)

0.323*** (0.086)

0.233*** (0.059)

0.319*** (0.086)

0.252*** (0.059)

0.309*** (0.086)

0.231*** (0.059)

Acquaintance

0.002** (0.001)

0.001 (0.001)

0.002** (0.001)

0.001 (0.001)

0.001** (0.001)

0.001 (0.001)

0.001** (0.001)

0.001 (0.001)

Partnership
initiative

Wissenschaft

excellence

0.355*** (0.130)

−0.138 (0.113)

0.358*** (0.130)

−0.131 (0.113)

0.355*** (0.131)

−0.150 (0.114)

0.354*** (0.131)

−0.148 (0.114)

0.227*** (0.055)

0.129*** (0.038)

0.218*** (0.055)

0.115*** (0.038)

0.194*** (0.054)

0.131*** (0.038)

0.185***(0.054)

0.112*** (0.038)

Patents

−0.000 (0.000)

−0.000 (0.000)

−0.000 (0.000)

−0.000 (0.000)

−0.000 (0.000)

−0.000 (0.000)

−0.000 (0.000)

−0.000 (0.000)

REC

VR China

HES

0.006* (0.003)

0.008*** (0.002)

0.005 (0.003)

0.006*** (0.002)

0.013*** (0.003)

0.010*** (0.002)

0.013*** (0.003)

0.011*** (0.002)

–

–

–

–

0.005** (0.003)

0.003* (0.002)

0.006** (0.003)

0.005*** (0.002)

−0.010*** (0.003) −0.002 (0.002)

−0.012*** (0.003) −0.005** (0.002)

–

–

–

–

CEE countries

−0.228*** (0.037) −0.131*** (0.022) −0.223*** (0.038) −0.124*** (0.022) −0.237*** (0.037) −0.131*** (0.022) −0.233*** (0.038) −0.125*** (0.022)

Project size

0.000 (0.000)

−0.000 (0.000)

0.000 (0.000)

−0.000 (0.000)

0.000 (0.000)

−0.000 (0.000)

0.000 (0.000)

−0.000 (0.000)

Consortium size

0.069*** (0.016)

0.074*** (0.011)

0.068*** (0.016)

0.073*** (0.011)

0.078*** (0.016)

0.075*** (0.011)

0.079*** (0.016)

0.077*** (0.011)

Constant

−2.879*** (0.387) −1.915*** (0.276) −1.888*** (0.353)

0.412** (0.238)

−3.374*** (0.432) −2.139*** (0.302) −2.947*** (0.255) −0.039 (0.162)

McFadden’s R2

AIC

Brant test

0.090

1.687

307.89***

0.091

1.685

298.41***

0.090

1.688

301.40***

0.092

1.686

306.60***

Notes: Multinomial logistic regression as in Eq. 1, with below threshold (1) being the reference category. Dummy variables for the different H2020 programs are included in all model
variants as controls. Asymptotic standard errors given in brackets; *** significant at the .01 Ebene, ** significant at the .05 Ebene, * significant at the .1 Ebene. The significant test statistic of the
Brant test confirms that the parallel regression assumption of an ordered logit model is violated.

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Tisch 6. Results of the OLS regression models based on expert scores

Degree

Eigenvector

Coord. Erfahrung

Acquaintance

(1)
0.452*** (0.062)

–

0.282*** (0.076)

−0.002** (0.001)

Partnership initiative

0.250 (0.137)

Science excellence

Patents

REC

VR China

HES

CEE countries

Project size

Consortium size

Constant

R2

AIC

0.120** (0.049)

0.000 (0.000)

0.008** (0.003)

–

−0.009*** (0.002)

−0.232*** (0.027)

−0.000*** (0.000)

0.151*** (0.014)

6.877*** (0.329)

0.091

5.040

(2)
–

0.516*** (0.062)

0.262*** (0.076)

−0.001** (0.001)

0.260* (0.137)

0.109** (0.049)

0.000 (0.000)

0.007** (0.003)

–

−0.012*** (0.002)

−0.222*** (0.027)

−0.000*** (0.000)

0.148*** (0.014)

10.571*** (0.294)

0.093

5.040

(3)
0.420*** (0.060)

–

0.272*** (0.076)

−0.002** (0.001)

0.231* (0.138)

0.105** (0.049)

(4)
–

0.463*** (0.059)

0.252*** (0.076)

−0.002** (0.001)

0.235* (0.138)

0.091* (0.049)

0.000 (0.000)

0.000 (0.000)

0.016*** (0.003)

0.008*** (0.002)

–

−0.237*** (0.027)

−0.000*** (0.000)

0.160*** (0.014)

6.263*** (0.364)

0.090

5.040

0.017*** (0.003)

0.009*** (0.002)

–

−0.230*** (0.027)

−0.000*** (0.000)

0.159*** (0.014)

9.436*** (0.203)

0.092

5.040

Notes: OLS regression based on expert scores as in Eq. 2. Dummy variables for the different H2020 programs are included in all model variants as controls.
Standard errors in parentheses; Asymptotic standard errors given in brackets; *** significant at the .01 Ebene, ** significant at the .05 Ebene, * significant at the .1 Ebene.

Darüber hinaus, following the results of our project status models, it seems that acquainted project
partners also show a slightly higher likelihood of being ranked on the main list, but with a low
marginal effect of one additional prior joint participation (see Table A3 in the Appendix). Auch,
having “strong ties” within the consortium does not significantly influence the likelihood of
achieving an assessment score “above threshold.” The expert score model shows for acquain-
tance even a significantly negative, but small, effect of one additional prior joint participation.
We achieve a similar result for project consortia linked to partnership (PPP) initiatives. Diese

Figur 2. Marginal effect of network centrality on proposal success. Predicted marginal effects for model variants (1) Und (2).

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Figur 3. Marginal effects of coordination experience (links) and partnership initiatives (Rechts) on proposal success. Conditional marginal ef-
fects for model variant (1).

consortia are also more likely to be on the main list, but are not necessarily more likely to
achieve the project evaluation “above threshold” (see also Figure 3, right plot).

Based on these slightly contradictory results, we can confirm our Hypothesis 2 only for the main
list models. The proposed positive relationship between coordination experience or acquaintance
and proposal success is significant when it comes to highly evaluated proposals with successful
outcomes that are finally eligible for FP funding. This result may indeed explain the tendency for
European research collaboration programs to produce closed clubs of familiar and experienced
project partners. Jedoch, it seems that knowing the funding procedures and the managerial tasks
related to project coordination is more important than prior acquaintance between the project
Partner (“strong ties”) for proposal success in the early stage of a collaboration.

Regarding the influence of research capabilities, we observe a clear pattern throughout all
model variants. The probability of submitting a successful proposal increases significantly with
the number of top universities in a consortium (Figur 4). It appears that scientific excellence is
indeed a success criterion for H2020. Jedoch, it should be noted that we cannot fully rule out
the existence of a selection bias here. It may be that so-called star universities are better able to

Figur 4. Marginal effect of scientific excellence on proposal success. Predicted marginal effects
for model variant (1).

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act strategically with respect to their collaboration choices. They can be more selective than univer-
sities with a poorer reputation, and may therefore only engage in projects for which they can assess
ex ante a high likelihood of success. In contrast to scientific capabilities, consortia with a broad
applied knowledge base (as measured by the number of patents) are not significantly more likely
to produce successful proposals. On this basis, the empirical results confirm our Hypothesis 3 nur
for scientific research capabilities, but not for more applied or technology-specific capabilities.

Beyond our main hypotheses, it is also worth discussing the results for the institutional com-
position of a consortium. The variables for the share of private companies (VR China) and the share of
universities (HES) are highly negatively correlated. This finding suggests that most of the H2020
project proposals take one of two forms: They are either more application oriented (with a high
share of companies) or more science oriented (with a high share of universities). The regression
results show that consortia with a high share of universities are significantly less likely to
achieve an evaluation score “above threshold” or to be listed on the main list. Im Gegensatz, con-
sortia dominated by private companies (VR China) and research organizations (REC) appear to have
a higher probability of proposal success. These findings seem to be in line with the general FP
goals and the H2020 orientation, which focuses on application-oriented research with imme-
diate societal relevance for the Societal Challenges pillar.

To further investigate this finding, we also include the ratio of the share of PRC to HES orga-
nizations as an alternative measure for the institutional composition of a consortium. Das
variable is a rough proxy for the balance of PRC and HES, or the relative application orientation
of a project. The average ratio of PRC to HES organizations across all projects is 1.5 (Table A1 in
the Appendix). We see that PRC and HES jointly applied for a project in 85% of the proposals,
while 1.5% of all proposals were submitted by consortia including only HES organizations and
0.5% by consortia including only PRC organizations (Table A4). The regression results
(Appendix, Table A5) confirm our finding that more application-oriented proposals have high
chances of success.

Endlich, of interest from a pan-European perspective is the significant and negative coefficient
for partners from Central and Eastern Europe (CEE). It suggests that the probability of achieving a
good project evaluation significantly decreases with the number of CEE partners in a consortium.
A one-unit increase in the number of CEE partners decreases the likelihood of being on the main
list by more than 1.5%.

6. CONCLUSIONS AND DISCUSSION

This study sheds light on the types of project consortia that are more successful in acquiring col-
laborative R&D funds within Horizon 2020, the latest edition of the EU’s largest research funding
scheme. The aim was to explore the systematic patterns underlying the allocation of research
funds across research organizations and regions in Europe. We hypothesized that, in addition to
content-specific factors, a consortium’s characteristics related to the social, Netzwerk, or institu-
tional compositions of a consortium can significantly influence its success in securing funding
for collaborative R&D projects. For the empirical analysis we used evaluation data (score and
project status) for all H2020 proposals, including funded and nonfunded proposals, die Waren
submitted to the Societal Challenges program pillar between 2014 Und 2016. Our study covers
project applications submitted by over 7,000 project consortia involving around 23,000
research organizations across Europe to systematically reveal those specific partner configura-
tions that are more likely to successfully pass the proposal stage.

We found that consortia achieve a significantly better evaluation score or project status if they
involve (A) a coordinator with experience in previous Framework Programmes, (B) a larger number

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of top universities with a high level of high scientific excellence and impact (d.h., established
reputation), Und (C) a large number of partners from Western Europe (former EU15). This success
profile of high experience and high reputation consortia appears to be in line with the general
H2020 program objectives, namely to be the primary program for research excellence in
Europa. Jedoch, when it comes to better integrating all EU member states—often claimed as
a core objective for an EU-level research funding program—this “widening” aspect cannot be con-
firmed by our results. Somit, H2020 funds consortia meeting the excellence criteria, but focuses
less on factors related to the cohesion or inclusion of actors across Europe. The fact that high-
quality connections between consortium partners (in terms of their eigenvector centrality), eher
than the pure number of project partners and linkages (in terms of degree centrality), positively
influence the proposal success would appear to be in line with these findings.

In view of H2020’s ambitions to promote excellence, it is interesting that consortia composed
of a high share of universities have significantly lower chances of success than application-
oriented consortia, even though universities show the highest participation intensity in H2020
Societal Challenges. Only consortia that include top universities outperform other consortia in
terms of proposal success. Im Gegensatz, H2020 funding criteria appear to discriminate against
consortia with a high university share, typically those more oriented towards discovery and fron-
tier research but at a lower Technology Readiness Level (TRL)8. Arguably, the Societal
Challenges pillar of H2020 appears to maintain the tradition of previous EU FP editions as a
funding program primarily designed for applied projects that promise economic or societal
returns in the short term. The fact that universities in particular show a higher tendency to col-
laborate among themselves, paired with the negative correlation between consortia dominated
by scientific organizations and those with a high share of private companies, points to a low
integration level of science and industry partners in the projects. Although closing the gap
between science and industry is one of the objectives of the multidisciplinary and transdisci-
plinary Societal Challenges pillar, this gap is still evident and seems to be reproduced by the eval-
uation and funding criteria at the project level. Jedoch, similar to predecessor FPs, Forschung
organizations involved in applied research seem to fill this gap. Consortia with a higher share of
application-oriented research organizations—the so-called FP core organizations—are more
successful in acquiring H2020 projects, as these organizations may fulfill the requested bridging
role between science and industry.

Our study is among the first to draw on nonsuccessful R&D projects to clearly differentiate
between proposals that are not able to pass the early collaboration stage, and successful proposals
that result in “‘real” R&D collaborations. Folglich, our results can be seen as an important
step towards dissecting and getting to the “core” of the H2020 network, with its associated struc-
tures of collaboration and knowledge diffusion across Europe. In this regard, proposal success for
policy-funded research could be explained by two factors: a quality factor, which is determined by
how well the project team manages to work together and develop a good proposal (scientific
capabilities and experience), and more systemic factors, related to the funding criteria and aims,
which favor application-oriented consortia constellations.

Although we were able to provide initial insights into both factors, new research strategies
involving large-scale control group methods or semantic methods (z.B., text mining procedures)

8 TRLs are used in H2020 to characterize the (technological) maturity of a research or innovation project, aus
basic research to market operations. Zum Beispiel, it is assumed that at a low TRL level the focus is on basic
research with knowledge transfer taking place mostly between research partners, while at a higher TRL level
more industrial partners are involved in prototyping, pilot, or demonstration projects, Zum Beispiel.

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for closer examinations of the proposal content would be necessary to shed more light on the pat-
terns of funding and the existence of a policy-induced bias in awarding research grants. Diese
issues, as well as the identification of a broader set of success factors, will be important topics
for future scientific research on the evaluation and impact of public research funding.

ACKNOWLEDGMENTS

We want to thank three anonymous referees for their useful comments and suggestions, Und
are grateful for the valuable feedback during the GeoInno 2018 and Sunbelt 2018 Konferenz.

BEITRÄGE DES AUTORS

Iris Wanzenböck: Konzeptualisierung, Formale Analyse, Untersuchung, Methodik, Aufsicht,
Validierung, Visualisierung, Writing—original draft, Writing—review & Bearbeitung. Rafael Lata:
Konzeptualisierung, Datenkuration, Formale Analyse, Untersuchung, Methodik, Visualisierung,
Writing—review & Bearbeitung. Doga Ince: Datenkuration, Formale Analyse, Methodik, Validierung,
Writing—review & Bearbeitung.

COMPETING INTERESTS

The authors have no competing interests.

FUNDING INFORMATION

Iris Wanzenböck benefited from funding of the INTRANSIT project funded by the Norwegian
research council.

DATA AVAILABILITY

In this study we used detailed microdata about proposals including personal data of the applicants.
Due to the confidentiality rules for Framework Programme data stored in eCORDA, we are not
allowed to publish any personal or sensitive data provided by the applicants.

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APPENDIX

Table A1. Descriptive statistics of the dependent and independent variables

Variable
Dependent variables

Project status

Expert score

Consortium factors

Degree (log)

Eigenvector (log)

Coord_experience

REC

VR China

HES

CEE countries

Acquaintance

Science excellence

Patents

Partnership

Controls

TPT

Energy

Umfeld

Food

Health

Sicherheit

Society (baseline)

Project size

Consortium size

Alternative variable

Ratio PRC:HES

Mean

Std.

2.473

9.393

5.361

−2.119

0.437

18.749

35.536

37.010

1.049

38.144

0.694

363.329

0.122

0.085

0.179

0.060

0.045

0.348

0.092

0.191

0.681

3.145

0.770

0.803

0.496

14.698

21.809

22.935

1.451

67.796

0.930

853.755

0.328

0.279

0.383

0.238

0.207

0.476

0.289

0.393

4,356,990

9.626

2,622,738

3.020

1.540

1.767

Min

1.000

0.000

1.504

−10.887

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

4.000

0.000

Max

3.000

15.000

7.168

−0.666

1.000

100.000

100.000

100.000

13.000

1,130.000

7.000

11,335.800

1.000

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1.000

1.000

1.000

1.000

1.000

1.000

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16.000

13.925

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Proposal success in Horizon 2020

Table A2. Correlation table

Coord.

Score Degree Eigenvector

experience REC

PRC HES

CEE Acquaintance

Scientific
excellence Patents Multila.

Financial
Größe

Partner
Größe

Project
Status

Project status 1.000

Score

Degree

Eigenvector

Coord.

Erfahrung

REC

VR China

HES

CEE

−.724 1.000

−.069

−.060

−.081

−.071

.095 1.000

.086

.956

1.000

.066

.258

.289

1.000

.071

.193

.155

.022

1.000

−.001 −.015 −.385

−.471

−.191

−.250 1.000

.041 −.038

.346

.455

.210

−.321 −.710 1.000

.081 −.072 −.209

−.217

−.128

−.089 −.028

.037 1.000

Acquaintance −.118

.067

.437

.394

Wissenschaft

excellence

−.070

.042

.406

.447

.168

.247

.228 −.195

.105 −.124

1.000

−.055 −.334

.409 −.141

.445

1.000

Patents

−.057

.057

.330

.240

.042

.167

.045 −.110 −.089

.397

.047

1.000

Partnership

−.103

.046 −.100

−.151

−.037

.030

.295 −.220 −.052

−.061

−.144

.026

1.000

Financial
Größe

.008 −.042

.043

.038

.008

.010

.141 −.111 −.064

.175

.145

.100

−.045

1.000

Partner size

−.088

.121

.054

.043

.035

.030 −.004 −.100

.207

.296

.168

.109

−.177

.272

1.000

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Degree

Main list
SE

dy/dx
0.001 0.006

Table A3. Marginal effect estimates for the MLR models

(1)
Above threshold/rejected
dy/dx

0.052

SE
0.010

Main list
SE

dy/dx
*** −0.004 0.006

Coord. Erfahrung

0.022 0.007 ***

0.043

0.012

***

0.021 0.007 ***

Acquaintance

0.000 0.000

**

0.000

0.000

0.000 0.000

*

Partnership initiatives

0.035 0.011 ***

−0.039

0.023

**

0.035 0.011 ***

Science excellence

0.016 0.005 ***

0.021

0.008

***

0.013 0.005 ***

Patents

0.000 0.000

0.000

0.000

0.035 0.011 ***

REC

VR China

HES

0.000 0.000

0.001

0.000

***

0.001 0.000 ***

–

–

–

–

0.000 0.000

*

−0.001 0.000 ***

0.000

0.000

–

–

CEE countries

−0.016 0.003 ***

−0.021

0.004

*** −0.017 0.003 ***

−0.021

Project size (financial)

0.000 0.000

0.000

0.000

0.000 0.000

Project size (Partner)

0.004 0.001 ***

0.014

0.002

***

0.005 0.001 ***

(2)

0.000

0.014

(4)

Main list

Above threshold/rejected

Main list

Above threshold/rejected

dy/dx

SE

dy/dx

SE

dy/dx

SE

dy/dx

SE

Eigenvector

0.004 0.006

0.071

0.011

*** −0.002 0.006

0.071

0.011

Coord. Erfahrung

0.021 0.007 ***

0.040

0.012

***

0.020 0.007 ***

Acquaintance

0.000 0.000 ***

0.000

0.000

0.000 0.000

*

Partnership initiatives

0.035 0.011 ***

−0.038

0.023

Science excellence

0.016 0.005 ***

0.018

0.008

*

**

0.035 0.011 ***

0.013 0.005 ***

Patents

0.000 0.000

0.000

0.000

0.000 0.000

REC

VR China

HES

0.000 0.000

0.001

0.000

***

0.001 0.000 ***

–

–

–

–

0.000 0.000

*

CEE countries

−0.016 0.003 ***

−0.001 0.000

−0.001

−0.020

0.000

*

–

–

0.005

***

0.020 0.007 ***

Project size (financial)

0.000 0.000

0.000

0.000

0.000 0.000

Project size (Partner)

0.004 0.001 ***

0.013

0.002

***

0.005 0.001 ***

0.040

0.000

−0.041

0.018

0.000

0.002

0.001

–

0.040

0.000

0.014

0.012

0.000

0.023

0.008

0.000

0.000

0.000

–

0.012

0.000

0.002

Notes: Marginal effects (dy/dx) are calculated as means of the remaining variables, based on multinomial logistic regression as in Eq. 1, with below threshold/
rejected (1) being the reference category. Dummy variables for the different H2020 programs are included in all model variants as controls. *** significant at the
.01 Ebene, ** significant at the .05 Ebene, * significant at the .1 Ebene.

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(3)
Above threshold/rejected
dy/dx

0.054

0.044

0.000

−0.042

0.022

−0.042

0.002

0.000

–

SE
0.010

0.012

0.000

0.023

0.008

0.023

0.000

0.000

–

0.004

0.000

0.002

***

***

*

***

*

***

***

***

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Proposal success in Horizon 2020

Table A4. Relative frequencies PRC and HES participation

Proposals including PRC and HES

Proposals without PRC

Proposals without HES

Proposals with HES only

Proposals with PRC only

Percentage of total proposals
85%

9%

6%

1.5%

0.5%

Table A5. Estimation results MLR models—Alternative measure of institutional composition (Verhältnis: relative application orientation)

Degree

Eigenvector

Main list
0.228*** (0.089)

–

(1)
Above threshold/rejected
0.337*** (0.056)

Main list
–

(4)
Above threshold/rejected
–

–

0.361*** (0.099)

0.471*** (0.064)

Coord. Erfahrung

0.328*** (0.089)

0.242*** (0.060)

0.306*** (0.089)

0.215*** (0.060)

Acquaintance

0.001* (0.000)

0.001 (0.000)

0.001* (0.001)

0.001 (0.001)

Partnership initiative

0.264* (0.143)

−0.248* (0.042)

0.264* (0.143)

−0.248* (0.122)

Science excellence

0.159*** (0.053)

0.111*** (0.037)

0.136** (0.054)

0.085** (0.038)

Patents

0.000 (0.000)

−0.000 (0.000)

0.000 (0.881)

−0.000 (0.235)

Ratio application orientation

0.069*** (0.027)

0.053*** (0.020)

0.089*** (0.028)

0.076*** (0.026)

CEE countries

Project size

Consortium size

Constant

McFadden’s R2

−0.245*** (0.039)

−0.122*** (0.022)

−0.238*** (0.030)

−0.115*** (0.022)

0.000 (0.000)

0.000 (0.000)

0.000 (0.000)

0.000 (0.000)

0.078*** (0.016)

0.079*** (0.518)

0.081*** (0.017)

0.082*** (0.012)

−4.008*** (0.511)

−2.384*** (0.330)

−2.057*** (0.271)

0.377** (0.170)

0.086

0.088

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