Dynamics of Inventor Networks and the Evolution of Technology Clusters

Abstract

Clusters are important drivers of regional economic growth. Although their benefits are well recognized, research into their evolution is still ongoing. Most real-world clusters seem to have emerged spontaneously without deliberate policy interventions, each cluster having its own evolutionary path. Since there is a significant gap in our understanding of the forces driving their evolution, this study uses a quantitative approach to investigate the role of inventor collaboration networks in it. Inventor collaboration networks for 30 top-performing American metropolitan clusters were constructed on the basis of patent co-authorship data. The selected clusters operate in hi-tech fields: information technology, communications equipment and the biopharmaceutical industry. Starting from a widely accepted hypothesis that the 'small-world' structure is an optimal one for knowledge spillovers and promotes innovation effectively, the authors statistically tested the impact of 'small-world'network properties on cluster innovation performance proxied by patent output. The results suggest that the effect of the small-world structure is not as significant as theorists hypothesized, not all clusters benefit from the presence of inventor collaboration networks, and cluster performance can be affected by policy interventions. Our analyses also suggest that cluster typology moderates the impact of inventor network properties on cluster innovation performance.

Does network position foster knowledge production? Evidence from international scientific collaboration network

“…A country with a high clustering coefficient and short characteristic path length in the collaboration network can have more knowledge output than others. Our empirical result is consistent with various empirical studies for the patent collaboration networks of 16 countries (Chen & Guan, ), interfirm collaboration networks on the basis of 11 high‐technology manufacturing industries alliance (Schilling & Phelps, ), and hi‐tech industry metropolitan clusters using inventor collaboration networks (He & Fallah, ). The small‐world structure increases trust and reduces distance between actors, and facilitates efficient and effective information transfer.…”

“…The clustering coefficient refers to the possibility of a specific node's neighbor nodes connecting to each other, and characteristic path length is defined as the average number of edges along the shortest paths between all pairs of actors (Uzzi & Spiro, 2005;Watts & Strogatz, 1998). The small-world quotient is used to measure the degree of the network's small-world nature and calculated by the clustering coefficient divided by the path length (Chen & Guan, 2010;He & Fallah, 2014;Uzzi & Spiro, 2005;Zhang, Guan, & Liu, 2014). Thus, it tends to be seen as the interaction term for the clustering coefficient and path length (Fleming et al, 2007;Schilling & Phelps, 2007).…”

“…The dependent variable, the number of publications, is count data. Because the conditional variance of the dependent variable obviously exceeds its conditional mean, namely existing overdispersion, we employ a negative binomial model instead of the Poisson model in our analysis (Araújo et al, 2018;Breschi & Lenzi, 2015;He & Fallah, 2014;Miguelez & Moreno, 2013;Ponds et al, 2010). In addition, the Hausman test rejects random effects specification.…”

“…Another stream has taken social network analysis technique, a promising tool for capturing the structure and dynamics of relational spillovers effects, to enrich the literature on knowledge spillovers (Araújo, Gonçalves, & Taveira, ; Boschma & Ter Wal, ; Breschi & Lenzi, ; Fleming, King, & Juda, ; Gluckler, ; Ter Wal & Boschma, ). The related literature has focused on the influence of network properties on innovation performance or inventive productivity, such as external linkages and gatekeepers (Breschi & Lenzi, ), internal reach, external reach and clique density (Araújo et al, ; Breschi & Lenzi, ), network density (De Noni, Orsi, & Belussi, ), betweenness centrality (Broekel, Brachert, Duschl, & Brenner, ), or small‐world structure (Fleming et al, ; He & Fallah, ). As Boschma () put it, geographical proximity is neither necessary nor sufficient conditions for knowledge spillovers to occur, while network relations may act as transmission channels for knowledge diffusion (Bathelt et al, ; Breschi & Lissoni, ; Maggioni at al., ; Miguelez & Moreno, ; Ponds et al, ).…”

“…As Boschma () put it, geographical proximity is neither necessary nor sufficient conditions for knowledge spillovers to occur, while network relations may act as transmission channels for knowledge diffusion (Bathelt et al, ; Breschi & Lissoni, ; Maggioni at al., ; Miguelez & Moreno, ; Ponds et al, ). There is a growing body of literature recognizing the significance of network structure (Araújo et al, ; Crespo, Suire, & Vicente, ; De Noni et al, ; He & Fallah, ). Following this logic, taking only the spatial spillover effects into consideration and neglecting the role of network structure are insufficient.…”