Exploring the dynamics of regional R&D networks: a closer look at Valencia's inter-organizational partnerships

[EN]: The literature on knowledge networks has long grappled with two types of questions. The first concerns the antecedents of tie formation; that is, how actors select their partners. The second concentrates rather on the implications of the resulting network structure for knowledge exchange and i...

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Detalles Bibliográficos
Autor: Yankova, Dima
Tipo de recurso: tesis doctoral
Estado:Versión publicada
Fecha de publicación:2023
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/386791
Acceso en línea:http://hdl.handle.net/10261/386791
Access Level:acceso abierto
Palabra clave:Knowledge network
R&D collaboration
Social proximity
Tie strength
Repeated collaboration
Red de conocimiento
Colaboración en I+D
Proximidad social
Vínculos fuertes
Colaboración repetida
Xarxa de coneixement
Col·laboració en I+D
Proximitat social
Vincles forts
Col·laboració repetida
Descripción
Sumario:[EN]: The literature on knowledge networks has long grappled with two types of questions. The first concerns the antecedents of tie formation; that is, how actors select their partners. The second concentrates rather on the implications of the resulting network structure for knowledge exchange and individual or collective performance. Many studies have acknowledged the critical role of social proximity as a driver of link formation and an important prerequisite for the transfer of both tacit and complex knowledge. Yet, scholarly understanding of social proximity as a concept remains somewhat constrained and the implications of building strong ties are subject to ongoing debates. Hence, the primary objective of this doctoral work is to address two sets of research questions. First, we aim to investigate how various forms of social proximity influence the formation of ties within knowledge networks. In this context, we differentiate between prior joint experiences in successful and unsuccessful project applications, as both forms of engagement constitute a source of relational embeddedness between actors. Second, we examine how the emerging strong bonds between organizations differ in their role and function. We test whether and under what conditions organizations leverage repeated collaborations to exploit the same topic multiple times (what we call specialization) or to explore new ones (diversification). These questions contribute to two separate streams of literature: the one on knowledge network dynamics by highlighting the origin and consequences of strong coupling; and the one on strategic management by tracing organizations’ strategic response to funding rejection. The thesis zooms in on Valencia’s regional publicly-funded R&D network. To conduct the empirical analysis, we build a unique dataset which contains information on all R&D partnerships, formed between 2016 and 2022, which requested public subsidy from one of the top two regional sources of innovation-related funding. The two entities together manage 75% of the 1.6 billion Euros designated for the implementation of the regional smart specialization strategy. Overall, this document introduces a new, vastly unexplored facet of social proximity, thus challenging existing assumptions on what type of former interaction is necessary to generate sufficient levels of trust and familiarity so as to motivate further engagement between actors. Moreover, it demonstrates empirically that structurally equivalent network ties can assume fundamentally distinct roles, leading either to thematic specialization or diversification. These findings suggest that the danger of overembeddedness in one type of activity after several collaborations may not necessarily be a product of the structural setting alone and the presence of strong ties. It is rather a product of organizations’ strategic choices about how they harness their strong bonds. The conclusions of this thesis hold far-reaching implications for policy design, and can guide policymakers in steering more effective network interventions.