Axis 1 focuses on the analysis of multiplex global business networks of different types. It explores inter- Investissement direct étranger relationships such as buyer-supplier networks, business-to-business alliance networks and intra-company networks.

The main objective of this research component is to examine how these networks evolve over time and to study their effects on companies’ strategy, economic performance and innovation.

Until recently, companies were perceived as atomistic actors responding rationally to market incentives. Over the last decade, with advances in institutional theory and the penetration of sociological perspectives in business literature, we have seen a shift toward what can be called “the relational side of business.”  Researchers have thus begun to pay attention to the relationships and interdependencies between firms and how they affect firms’ strategies, performance and innovations.

Nevertheless, much of this literature is conceptual; empirical research has lagged behind. As a result, our understanding of the structure and temporal dynamics of the global network composed by the linkages between firms is rather limited. Very little research exists on how firm linkages have evolved at the local scale compared with the trans-local scale. Even less is known about how the typology of these linkages (horizontal and vertical, intra-firm and inter-firm) has changed over time and how it affects firm performance and innovation (Glückler, 2007; Ter Wal and Boschma, 2009). There are two reasons for the persistence of these gaps: (1) lack of data and (2) empirical analysis of large-scale linkages, which requires both a specific network methodology and an analysis of large networks.

In this axis, the goal of the research is to compile unstructured data in order to create a database on inter-firm linkages and to analyze issues related to the development and evolution of inter-firm networks. For example, the research will analyze how external shocks, such as the recent coronavirus event or the global financial crisis, affect the characteristics and dynamics of inter- organizational networking and will explore which parts of the network are more resistant to external shocks, both structurally and geographically. Another component aims to study the influence of personal links between co-inventors using global analysis of patents and scientific publications. Obtaining this information will let the researchers at the chair build a global multiplex network composed of several layers.

The analysis of such multiplex networks is very important to understand the interrelationships and contingencies between the personal and organizational levels. For example, it is important to determine whether having one type of arrangement increases the likelihood of having another, and how this change affects business performance and innovation.

It is also important to situate the main innovations within the global social fabric (as measured by patent citations) and to analyze how differences in the structural environment of a multiplex network can explain differences in innovations.

In addition, this line of research explores gender differences in these types of networks, given that differences between males and females in science, technology and innovation have drawn considerable scholarly interest over the past two decades. Researchers argue that there is strong evidence of gender inequality in scientific research and patenting.

The second axis explores how global interorganizational networks (discussed in Axis 1) are transforming geographic locations, industrial clusters, and the spatial dynamics of industries. Economic regionalization leads to increased regional and local specialization because the reduction in transportation costs and trade barriers allow firms to cluster together to benefit from local economies of scale (Krugman, 1991; Fujita, Krugman, Venables, 2001), which in turn are expected to increase local productivity growth (Martin and Sunley, 1998). Industrial clustering has long been recognized as a driver of regional economic development and regional competitiveness.

Numerous studies have explored the link between geographic clustering of firms and regional performance, including knowledge and innovation creation, entrepreneurship, and job creation (Delgado et al., 2010, 2014; Feldman and Audretsch, 1999; Porter, 2003; Porter, 1998; Bresnahan and Gambardella, 2004).

Given the importance of industrial clustering, various researchers have attempted to analyze the factors that influence the performance of industrial clusters. Much of the earlier literature focused on the benefits of clustering and the characteristics of clusters such as their size and age. Nevertheless, recent studies have argued that the positive benefits of clustering do not depend simply on the size and age of a cluster, but rather on the pattern of linkages among firms within clusters or, in other words, the intensity with which firms collaborate within the boundaries of a cluster (Maskell and Lorenzen, 2004).

In addition, experts observed that firms are increasingly linking together outside the geographic boundaries of a cluster to connect to global production and innovation systems.

External investment and vertical supply chain formation with suppliers in other clusters help reduce firms’ costs (Sturgeon et al., 2008), while horizontal partnerships (e.g., joint R&D projects) with firms in other clusters facilitate access to knowledge that is not available within the firm’s own cluster (Bathelt et al., 2004; Owen-Smith and Powell, 2004). Recent theoretical work has suggested that the success of an industrial cluster depends on the configuration of its local and translocal linkages (Bathelt et al., 2004; Lorenzen and Mudambi, 2013; Wolfe and Gertler, 2004). However, most of this literature is conceptual and empirical research has lagged behind.

This line of research further explores how inter-firm linkages transform industrial clusters. It also examines the relationships and impacts between clusters within industries and in new industries such as artificial intelligence and green industry.

This research will help identify structural gaps and opportunities between sectors; information that will be very important for policy makers. By using this mapping of linkages and innovations (as measured by patents), they will be able to make more informed and refined decisions.

The third axis focuses on methodological advances in social network analysis that can be applied to problem solving in the business literature. Given that most of the new approaches and theoretical frameworks of social networks exist only in the physical or sociological literature, it is crucial that these approaches and theoretical frameworks be developed and adapted for use by business researchers.

This axis provides business researchers with new tools and instruments for analyzing social networks. This axis is also important because it provides the first and second axes with new methodological tools.