Research, technology, 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 innovation 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 outcomes, and additionality effects of publicly funded research grants and partnerships (see, for example, Bornmann, Leydesdorff, & Van den Besselaar, 2010; Luukkonen, 2000; Viner, Powell, & Green, 2004). A prominent example in the scientific literature is the research and development (R&D) projects 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), 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). In contrast, organizations 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. However, 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 underrepresented 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. However, 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), we 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) the 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; and (c) the 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 (projects) 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: First, we use data on the status of the project proposal (main list, above threshold, below threshold) in a multinomial logistic regression (MLR) framework, and second, we make use of proposal evaluation 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 particular EU FPs, for several reasons: First, we draw on a unique and comprehensive data set including all rejected H2020 project proposals to differentiate between successful project applications, as typically identified in the literature, and unsuccessful ones. Second, with the focus on the project consortia rather than specific organizations, we provide novel evidence of the proposal success factors as they relate to entire project teams participating in H2020. Third, our 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: Section 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. Section 3 introduces the scale and scope of the H2020 database and our proposal success measures, before Section 4 explains the empirical modeling strategy. Section 5 presents the regression results, and Section 6 concludes with a synthesis and some policy implications derived from our empirical results.