Proximity and the Evolution of Collaboration Networks: Evidence from R&D projects within the GNSS industry

International audienceThis paper analyses the influence of proximity on the evolution of collaboration networks. It determines empirically how organizations choose their partners according to their geographical, cognitive, organizational, institutional and social proximity. Relational databases are constructed from R&D collaborative projects, funded under the European Union 6th Framework Programme within the navigation by satellite industry (GNSS) from 2004 to 2007. The stochastic actor-based model SIENA is used to model the network dynamic as a realisation of a continuous-time Markov chain and to estimate parameters for underlying mechanisms of its evolution. Empirical results show that geographical, organizational and institutional proximity favour collaborations, while cognitive and social proximity do not play a significant role

Getting Into Networks and Clusters: Evidence on the GNSS composite knowledge process in (and from) Midi-Pyrénées

This paper aims to contribute to the empirical identification of clusters by proposing methodological issues based on network analysis. We start with the detection of a composite knowledge process rather than a territorial one stricto sensu. Such a consideration allows us to avoid the overestimation of the role played by geographical proximity between agents, and grasp its ambivalence in knowledge relations. Networks and clusters correspond to the complex aggregation process of bi or n-lateral relations in which agents can play heterogeneous structural roles. Their empirical reconstitution requires thus to gather located relational data, whereas their structural properties analysis requires to compute a set of indexes developed in the field of the social network analysis. Our theoretical considerations are tested in the technological field of GNSS (Global Satellite Navigation Systems). We propose a sample of knowledge relations based on collaborative R&D projects and discuss how this sample is shaped and why we can assume its representativeness. The network we obtain allows us to show how the composite knowledge process gives rise to a structure with a peculiar combination of local and distant relations. Descriptive statistics and structural properties show the influence or the centrality of certain agents in the aggregate structure, and permit to discuss the complementarities between their heterogeneous knowledge profiles. Quantitative results are completed and confirmed by an interpretative discussion based on a run of semi-structured interviews. Concluding remarks provide theoretical feedbacks.Knowledge, Networks, Economic Geography, Cluster, GNSS

How do Clusters/Pipelines and Core/Periphery Structures Work Together in Knowledge Processes?

This paper contributes to the empirical identification of geographical and structural properties of innovative networks, focusing on the particular case of Global Navigation Satellite Systems (GNSS) at the European level. We show that knowledge bases of organizations and knowledge phases of the innovation process are the critical factors in determining the nature of the interplay between structural and geographical features of knowledge networks. Developing a database of R&D collaborative projects of the 5th and 6th European Framework Programs, we propose a methodology based on social network analysis. Its originality consists in starting from a bimodal network, in order to deduce two affiliation matrixes that allow us to study both the properties of the organization network and the properties of the project network. The results are discussed in the light of the mutual influence of the cognitive, structural and geographical dimensions on knowledge production and diffusion, and in the light of the knowledge drivers that give rise to the coexistence of a relational core-periphery structure with a geographical cluster and pipeline structure.Economic Geography, Knowledge networks, Social network analysis, EU Framework Programs, GNSS

A relational approach to knowledge spillovers in biotech. Network structures as drivers of inter-organizational citation patterns

In this paper, we analyze the geography of knowledge spillovers in biotech by investigating the way in which knowledge ties are organized. Following a relational account on knowledge spillovers, we depict knowledge networks as complex evolving structures that build on pre-existing knowledge and previously formed ties. In economic geography, there is still little understanding of how structural network forces (like preferential attachment and closure) shape the structure and formation of knowledge spillover networks in space. Our study investigates the knowledge spillover networks of biotech firms by means of inter-organizational citation patterns based on USPTO biotech patents in the years 2008-2010. Using a Stochastic Actor-Oriented Model (SAOM), we explain the driving forces behind the decision of actors to cite patents produced by other actors. Doing so, we address directly the endogenous forces of knowledge dynamics.knowledge spillovers, network structure, patent citations, biotech, proximity

The Dynamics of Interfirm Networks along the Industry Life Cycle: The Case of the Global Video Games Industry 1987-2007

In this paper, we study the formation of network ties between firms along the life cycle of a creative industry. We focus on three drivers of network formation: i) network endogeneity which stresses a path-dependent change originating from previous network structures, ii) five forms of proximity (e.g. geographical proximity) which ascribe tie formation to the similarity of actors' attributes; and (iii) individual characteristics which refer to the heterogeneity in actors capabilities to exploit external knowledge. The paper employs a stochastic actor-oriented model to estimate the - changing - effects of these drivers on inter-firm network formation in the global video game industry from 1987 to 2007. Our findings indicate that the effects of the drivers of network formation change with the degree of maturity of the industry. To an increasing extent, video game firms tend to partner over shorter distances and with more cognitively similar firms as the industry evolves.network dynamics, industry life cycle, proximity, creative industry, video game industry, stochastic actor-oriented model

Do scientific capabilities in specific domains matter for technological diversification in European regions?

Do scientific capabilities in regions translate into technological leadership? This is one of the most pressing questions in academic and policy circles. This paper analyzes the matching of scientific and technological capabilities of 285 European regions. We build on patent and publication records to identify regions that lie both at the scientific and technological frontiers (strongholds), that are pure scientific leaders, pure technological leaders, or just followers in 18 domains. Our regional diversification model shows that local scientific capabilities in a domain are a strong predictor of the development of new technologies in that domain in regions. This finding is particularly relevant for the Smart Specialization policy because it implies that the analysis of domain-specific scientific knowledge can be a powerful tool to identify new diversification opportunities in regions.publishedVersio

Socio-economic and technological flexibility is key to the resilience of American cities in times of crisis.

The Great Recession and its aftermath have seen new challenges for many American cities. In new research, Pierre-Alexandre Balland, David Rigby and Ron Boschma look at why some cities are more resilient to challenges and crises than others. They write that cities vary in their response to technological crises because of differences in their socio-economic flexibility. They argue that cities which are able to deploy inventors to new developing technological activities are better placed to avoid crises, and to cope with them when they do occur

Complementary interregional linkages and Smart Specialisation: an empirical study on European regions

Regional capabilities are regarded a pillar of Smart Specialisation Strategy (S3). There is yet little focus in S3 policy on the role of interregional linkages. This study on 292 NUTS-2 regions in Europe finds that interregional linkages have a positive effect on the probability of regions to diversify, especially in peripheral regions. What matters is not being connected to other regions per se, but being connected to regions that provide complementary capabilities. Finally, we propose a new indicator that enables regions to identify other regions as strategic partners in their S3 policy, depending on the presence of complementary capabilities in other regions.publishedVersio

Network dynamics in collaborative research in the EU, 2003– 2017

A key objective of the EU Framework Programmes for Research and Innovation is the creation of cross-country research networks. We make use of Social Network tools to describe the evolution of the EU research network across countries on the basis of unique data covering collaborative projects launched during the first four years of implementation of Horizon 2020 and its predecessor programmes, the Sixth and Seventh Framework Programme. We describe the positioning of all EU-countries in the collaborative research network, the positioning of the older member EU-15 and the newer member EU-13 countries in particular, and to what extent the network has been subject to change during the period 2003–2017. EU-15 and EU-13 countries have become more integrated, and some organizations fulfil a bridging function in the EU research network. EU-13 countries are more heavily engaged in parts of the programme on lower complexity research activities.publishedVersio

relatedness, complexity and regional inequality in Europe

Pinheiro, F. L., Balland, P-A., Boschma, R., & Hartmann, D. (2022). The dark side of the geography of innovation: relatedness, complexity and regional inequality in Europe. Regional Studies, 1-16. https://doi.org/10.1080/00343404.2022.2106362------------------------Funding: Flavio L. Pinheiro acknowledges the financial support provided by FCT Portugal under the project UIDB/0415s2/2020 – Centro de Investigação em Gestão de Informação (MagIC). Dominik Hartmann would like to express his gratitude for the financial support of CNPq [grant numbers 406943/2021-4 and 315441/2021-6]. Ron Boschma acknowledges the support from the European Union-funded PILLARS (Pathways to IncLusive LAbouR marketS) project [grant agreement number 101004703]. Pierre-Alexandre Balland acknowledges the financial support from the MSCA-RISE TREND (Transition with Resilience for Evolutionary Development) project [grant agreement number 823952].As regions evolve, their economies become more complex, and they tend to diversify into related activities. Although there is a bright side to this diversification process in terms of economic development, there may also be a dark side to it, as it possibly contributes to regional inequalities. The paper uses data on industries and patents to analyze the diversification patterns of 283 regions in 32 European countries over the past 15 years. We find that only the most economically advanced regions have the opportunity to diversify into highly complex activities. These regions tend to focus on related high-complex activities, while lagging regions focus on related low-complex activities, creating a spatial inequality feedback loop. This pattern creates a wicked problem for innovation policy: the strategy needed to improve the innovativeness of the European knowledge system might disproportionately benefit regions that are already developed and foster disparities.preprintpublishersversionepub_ahead_of_prin