Andrea Ancona

Science and Technology Parks (STPs) are recognized as key drivers of regional development, although their relationship with regional entrepreneurial ecosystems (EEs) remains underexplored, particularly in terms of longitudinal and comparative analyses. This study addresses these gaps by examining how participation in a global STP network impacts regional EE development. Using a longitudinal dataset on STPs’ entry and exit dynamics in the International Association of Science and Technology Parks and Areas of Innovation (IASP) across multiple European regions, we analyze the effects of joining a global STP network on key EE dimensions. To do so, we apply a non-parametric generalization of the difference-in-differences estimator for time-series cross-sectional data. The results reveal that joining IASP strengthens the intermediary dimension of regional EEs where parks are located, facilitating talent attraction and enhancing cross-regional knowledge spillovers. These effects are both immediate and context-dependent, with more pronounced impacts observed in regions with lower GDP per capita and outside the European Union. Theoretically, the study advances the STP literature by adopting a dynamic, macro-regional perspective, extending micro-level findings and linking STPs’ increased R&D efficiency to international collaboration. Additionally, it bridges STP and EE literatures, emphasizing the role of STPs as catalysts of regional EE development. Practically, our findings provide insights for policymakers and STP managers, highlighting the importance of supporting not only the creation of STPs but also their participation in global networks to foster innovation, especially in regions with fragmented or underdeveloped policy frameworks.

The selection of partners plays a crucial role in determining the success of collaborative projects. This study makes a valuable contribution to the existing literature on collaborative research and development by examining the impact of different partner selection strategies on the funding received in collaborative research projects. Previous studies have not shown sufficiently how collaborating with new partners as opposed to existing ones influences the amount of funding received. To bridge this gap, we use the innovative dual-projection approach from Social Network Analysis. Specifically, we analyze the network structure of projects funded by Horizon 2020, the eighth European Framework Programme. Our findings show that compared to collaborating with established partners, collaborating with new ones increases the likelihood of securing more funding when entering a new project. Moreover, projects coordinated by private or public organizations rather than research centers and higher education institutions have a higher probability of obtaining greater funding. However, the significance of partner connections diminishes with decreasing proximity to the focal organization. Ultimately, our results offer valuable insights into the effectiveness of the European Research and Technological Development Policy in fostering excellent science through cross-collaboration among a diverse group of actors.

The concept of collaborative R&D has been increasing interest among scholars and policy-makers, making collaboration a pivotal determinant to innovate nowadays. The availability of reliable data is a necessary condition to obtain valuable results. Specifically, in a collaborative environment, we must avoid mistaken identities among organizations. In many datasets, indeed, the same organization can appear in a non-univocal way. Thus its information is shared among multiple entities. In this work, we propose a novel methodology to disambiguate organization names. In particular, we combine supervised and unsupervised techniques to design a "hybrid" methodology that is neither fully automated nor completely manual, and easy to adapt to many different datasets. Thus, the flexibility and potential scalability of the methodology make this paper a worthwhile contribution to different research fields. We provide an empirical application of the methodology to the dataset of participants in projects funded by the first three European Framework Programmes. This choice is because we can test the quality of our procedure by comparing the refined dataset it returns to a well-recognized benchmark (i.e., the EUPRO database) in terms of the connection structure of the collaborative networks. Our results show the advantages of our approach based on the quality of the obtained dataset, and the efficiency of the designed methodology, leaving space for the integration of affiliation hierarchies in the future.

Entrepreneurial ecosystems are wealthy environments in which entrepreneurs, firms, and governments can operate frictionless, contributing to innovation and economic growth. The investigation of the structure of such systems is an open issue. We provide insights on this aspect through the formulation of seven network-based principles associating specific network metrics to distinct structural features of entrepreneurial ecosystems. In this way, we aim to support the measurement of the structural characteristics of an entrepreneurial ecosystem and the design of policy interventions in case of unmet properties. The proposed methodology is applied to an original network built on the relationships occurring on Twitter among 612 noteworthy start-ups from seven different European countries. This is a novel way to conceptualize entrepreneurial ecosystems considering online interactions. Thus, this work represents a first attempt to analyze the structure of entrepreneurial ecosystems considering their network architecture to guide policy-making decisions. Our results suggest a partial ecosystem-like nature of the analyzed network, providing evidence about possible policy recommendations.