Innovative energy technologies are key for the energy transition, but a broad uptake in society is still missing. The diffusion of innovations literature claims that network characteristics and geographical proximity support interpersonal communication that diffuses innovations in society. Both social network analysis and the geography of innovation propose that a balance of diversity and closeness is needed for innovation but their approaches have not been fully integrated. While social network studies focus on the characteristics of actors in the network and their degree of connectivity and diversity, the geography of innovations pays attention to the spatial distance between organisations and complementing geographical proximity with other dimensions.

This work integrates these two fields by measuring different proximity dimensions in the networks formed by successful information exchanges leading to the diffusion of energy technologies in Switzerland. By surveying 157 professionals and 3,000 adopters, we analysed the interactions of cognitive, organisational, institutional and social proximity with geographical proximity and theoretically connected the results with diversity and connectivity.

Our findings show that the networks among professionals have low geographical proximity and connected actors are very homogeneous. Networks between professionals and adopters are, on the other hand, more geographically proximate and diverse. Finally, networks among adopters and personal contacts show the highest geographical and social proximity. When aligning the geography of innovation’s proximities with the diversity and connectivity concepts of social network analysis, networks progressively rely more on connectivity-based dimensions the further they are in the innovation-diffusion process.

The aim of this paper is to present a framework for analyzing the effects of actor coordination modalities on the management of protected natural areas that lead to valorize its outstanding values. This framework consists of carrying out social network analyses on the scale of the governance of these areas and on the scale of business networks. We apply this framework to two UNESCO World Heritage goods of Massif Central (France): the Chaîne des Puys - Limagne fault tectonic arena and the Causses and the Cévennes, Mediterranean agro-pastoral Cultural Landscape. Relational data were collected by sociometric interviews (131) with actors involved in the governance arrangements (institutional actors) and in the valorization arrangements of the recognition outstanding value of the goods (private actors, mainly from the tourism sector). These data are longitudinal (3 to 5 periods), multiplex (6 to 11 types of links), and characterized by several variables (frequency, quality, purpose, personal and/or professional). The results highlight the social and organizational innovations along with the geographic and organized proximity dynamics that lead to changes in the centrality of actors and in the overall structure of networks (increased density and reciprocity), that have direct impacts on the management of protected natural areas. Beyond the qualitative explanations of the descriptive results of the global network structure and the positioning of the actors, we aim to improve the explanatory power of the quantitative relational data through stochastic and dynamic networks analyses. Finally, we discuss the interest and limitations of our analytical framework, and then outline research perspectives.

For many years, researchers in organizational studies and management studied interorganizational networks through interlocking directorates. Scholars examined the causes and consequences of such corporate networks including processes such as the diffusion of innovations, the behavior of corporate elites, and strategies for avoiding competition. Researchers have also highlighted the effects of interlocking directorships in various industries on foreign direct investment, international expansion, and relocation decisions. While much of the existing literature looked at the consequences of globalization on corporate behaviors, and the cohesiveness of the business community, there is a gap in research that addresses recent technological changes and how the business community responded to innovative and powerful newcomers. How did the rise of new technology companies impact corporate networks in established industries?

We focus on the automotive industry and financial services and explore how established companies, such as traditional automotive manufacturers and banks, utilize interlocks to connect with new competitors, such as electric vehicle producers and fintech firms, which are developing innovative business models and technological solutions. In this paper we mainly focus on financial service firms and use the automotive industry as a reference point. We examine how the network positions of these companies evolve over time and investigate their connectivity. By utilizing a global database of interlocking directorates, we monitor these developments across international borders and over time. Methodologically, we apply various measures of centrality and segregation.

We contribute to the ongoing discussion regarding interorganizational interlocking directorates as tools for strategic positioning in dynamic markets and environments. Ultimately, we seek to provide insights into whether interorganizational interlocking directorates are systematically utilized to evaluate potential new competitors and their technological innovations.

Over the past decades, economic crises, technological changes, demographic shifts, and migration flows have exacerbated the wealth gap between different social groups in Europe. From a social network perspective, the root of this inequality lies in the segregated network structure, known as assortative mixing, where nodes in networks tend to be connected to other nodes that are similar to them in some way. One type of such is the assortativity observed along the socioeconomic dimension. The causes of this socioeconomic assortativity are debated. It may stem not only from individual preferences but also from the opportunities available to individuals. To this end, this study examines the observed assortativity in acquaintance networks and seeks an answer to the question of to what extent preferences or opportunities drive it.

This study uses Spain – with its decentralized autonomous community system and diverse geographical and economic regions– as a case study. By analyzing aggregated relational data on the composition of acquaintanceship networks and the regional opportunity structures, the findings reveal significant regional variation in socioeconomic assortativity patterns. Moreover, individuals with low socioeconomic status exhibit stronger socioeconomic assortativity in densely populated areas, while individuals with high socioeconomic status maintain ties with peers in similarly prestigious occupations, regardless of their local context. This inequality can also be exacerbated in some regions with better economic conditions. The results disclose the role of SES-based heterogeneity in reinforcing inequality through tie formation, shedding light on how regional contexts can shape broader patterns of social structure.

The process of novelty adoption –technologies, medicines, cultural trends, or behaviors– relies on the interplay of individual decision-making and the structural properties of the underlying network. However, innovation adoption and social contagion have seldom been embedded in space, and much less so in cities. Taking solar energy infrastructure adoption as a spatially mediated diffusion process, this contribution proposes to model how individual behaviors –the decision to go solar– aggregate into city-wide patterns. To this end, we develop an adoption model that blends complex social (e.g., threshold-based) and spatial contagion models to mimick the process of rooftop solar panel installation in residential areas. Besides the possible social and spatial drivers, these models account as well for local governments efforts to incentivize rooftop solar panel installation, i.e. tax reductions and rebates, which represent a global (and, in this sense, a "non-spatial") stimulus to the spread of this particular innovation. Finally, the model is confronted with empirical data. This is done collecting public data on solar panel installation, together with cadastral GIS data, and training a computer vision classifier to recognize the presence of solar panels on aerial imagery. Applying this machinery to longitudinal data (2010-2023), we are capable of validating the proposed framework and proceed to answer important questions, such as which are the potential targeted strategies to amplify solar energy adoption, or under which circumstances innovation diffusion is the result of socio-spatial pressures, ore else policy-driven (incentive) process.