Dottorato in MERCATI E ISTITUZIONI PER LO SVILUPPO
E L'INNOVAZIONE

Titolo della tesi: A network-based approach to the analysis of entrepreneurial ecosystems and collaborative R&D

The emerging paradigms of open innovation have increasingly determined the need to collaborate for generating and disseminating knowledge, as well as developing technological innovation. Nowadays, innovation rarely occurs in isolation. Research and Development (R&D) investments frequently include the presence of partners, producing a growing interest in collaborative R&D. At the same time, the increasing interconnectedness among firms, governments, entrepreneurs, research institutes, and universities has prompted scholars to adopt an ecosystem approach in analyzing collaborative and innovation processes. This approach indeed, enables the simultaneous investigation of social, cultural, economic, and technological factors affecting the entrepreneurial process. In this context, the notion of economic complexity has gained relevance in the literature. The adoption of this perspective allows for the reduction of the computational dimension of socio-economic phenomena, while preserving more information than traditional approaches. Within the realm of economic complexity, economic networks play a significant role. The origins of research on economic networks can be traced back to two distinct disciplines: sociology and physics. The use of networks in the economic field is crucial to analyze the dynamic interactions among groups of heterogeneous actors, providing insights into their systemic behavior, and the link with the underlying structural network properties. This thesis aims to contribute to this strand of research by employing network theory with a precise focus on collaborative environments and innovation systems. Collaborative networks are indeed a popular concept in innovation and R&D literature. According to this representation, distinct organizations correspond to the network nodes, while edges stand for collaborative relationships between them. Thus, the idea behind this research is that a network science approach can unveil behavioral patterns within innovation systems, and contribute to shedding light on the interaction dynamics characterizing inter-organizational contexts. In particular, Social Network Analysis (SNA) is one of the most popular methods to examine relationships among social groups and entities through the analysis of their connection structure and the computation of centrality measures. The thesis is organized as follows. The first chapter investigates the concept of entrepreneurial ecosystem through the lens of network theory. In particular, it contributes to the existing body of literature by providing insights into the structural features of such systems. The main outcome of the first chapter is the formulation of seven network-based principles associating specific network metrics with distinct structural features of entrepreneurial ecosystems. These metrics are aimed to support the measurement of an entrepreneurial ecosystem and the design of policy interventions in case of unmet properties. The proposed methodology is then applied to an original network of European start-ups on Twitter. This case study represents a further contribution to the field by presenting a novel way to conceptualize entrepreneurial ecosystems, considering online interactions among actors. The results suggest a partial ecosystem-like nature of the analyzed network, providing evidence about possible policy recommendations. The second chapter is devoted to the study of different partner selection mechanisms in collaborative research networks. In particular, it contributes to the collaborative R&D literature by exploring the effect of collaborating with new over existing partners on the amount of funding received from the European Commission within the eighth EU Framework Programme (FP), Horizon 2020 (H2020). Specifically, this chapter employs an innovative technique in SNA, known as the “dual-projection” approach. This method was introduced by Everett and Borgatti specifically for analyzing two-mode social network data, i.e., data characterizing bipartite networks. The results from the econometric analysis show that partnering with new organizations increases the probability of obtaining more funds when entering a new project compared to collaborating with already existing partners. Additionally, projects coordinated by private or public organizations appear more likely to secure higher funding compared to research centers and higher education institutions. However, the relevance of partners’ connections decreases as we move away from the focal organization. These findings provide valuable insights into the effectiveness of the European Research and Technological Development Policy. Finally, the third chapter focuses on the analysis of collaboration patterns and participation dynamics in projects funded by the first eight EU FPs, from FP1 to H2020. It combines elements from SNA and statistics through the lens of economic complexity by computing participants’ centrality and assessing the stochasticity of the process, further estimating the probability of transitioning between classes of centrality across consecutive FPs. In doing so, this chapter contributes to the literature on the dynamics of collaborative research projects, particularly EU-funded ones, by shedding light on the Markovian nature of the collaboration dynamics over an extended period. The results reveal a quasi-Markovianity of participation dynamics, opening up opportunities for accurate forecasting procedures to estimate the leaders of future FPs. “Preferential attachment” mechanisms emerge, thus confirming the relevance of participating in EU-funded projects to strengthen organizations’ popularity. On the other hand, this finding highlights the pressing need to address “oligopolistic” behaviors linked to European funds that hinder the full realization of the European Research Area (ERA). The crucial role of European policies is emphasized by the estimated transition probabilities, which are influenced by breakthrough events in the EU research framework like the Treaty of Maastricht and the promotion of the ERA. However, sustained efforts are necessary to ensure a certain degree of openness and the “democratization” of European research funds.