Socio-technical networks of infrastructure management: Network concepts and motifs for studying digitalization, decentralization, and integrated management.

Networked infrastructure systems - including energy, transportation, water, and wastewater systems - provide essential services to society. Globally, these services are undergoing major transformative processes such as digitalization, decentralization, or integrated management. Such processes not only depend on technical changes in infrastructure systems but also include important social and socio-technical dimensions. In this article, we propose a socio-technical network perspective to study the ensemble of social actors and technical elements involved in an infrastructure system, and their complex relations. We conceptualize structurally explicit socio-technical networks of networked infrastructure systems based on methodological considerations from network analysis and draw on concepts from socio-technical system theories and social-ecological network studies. Based on these considerations, we suggest analytical methods to study basic network concepts such as density, reciprocity, and centrality in a socio-technical network. We illustrate socio-technical motifs, i.e., meaningful sub-structures in socio-technical networks of infrastructure management. Drawing on these, we describe how infrastructure systems can be analyzed in terms of digitalization, decentralization, and integrated management from a socio-technical network perspective. Using the example of urban wastewater systems, we illustrate an empirical application of our approach. The results of an empirical case study in Switzerland demonstrate the potential of socio-technical networks to promote a deeper understanding of complex socio-technical relations in networked infrastructure systems. We contend that such a deeper understanding could improve management practices of infrastructure systems and is becoming even more important for enabling future data-driven, decentralized, and more integrated infrastructure management.

Multi-level network dataset of ten Swiss wetlands governance cases based on qualitative interviews and quantitative surveys.

The dataset of this paper originated from quantitative online surveys and qualitative expert interviews with organizational actors relevant to the governance of ten Swiss wetlands from 2019 till 2021. Multi-level networks represent the wetlands governance for each of the ten cases. The collaboration networks of actors form the first level of the multi-level networks and are connected to multiple other network levels that account for the social and ecological systems those actors are active in. 521 actors relevant to the management of the ten wetlands are included in the collaboration networks; quantitative survey data exists for 71% of them. A unique feature of the collaboration networks is that it differentiates between positive and negative forms of collaboration specified based on actors' activity areas. Therefore, the data describes not only if actors collaborate but also how and where actors collaborate. Further additional two-mode networks (actor participation in forums and involvement in other regions outside the case area) are elicited in the survey and connected to the collaboration network. Finally, the dataset also contains data on ecological system interdependencies in the form of conceptual maps derived from 34 expert interviews (3-4 experts per case).

Social network data of Swiss farmers related to agricultural climate change mitigation.

We present social network data of Swiss farmers, focusing on exchange and advice relations regarding agricultural climate change mitigation. The data were generated via face-to-face interviews in 2019 using the survey software Network Canvas (https://networkcanvas.com). We interviewed 50 farmers, with 25 of these participating in a regional climate protection initiative in Switzerland as well as 25 farmers located in the same region who did not participate in the initiative. Farmers were asked to indicate the persons with whom they regularly exchanged on topics related to climate change and mitigation in agriculture. The farmers assessed the type and strength of their relationships and were asked to rate the knowledge of their contacts regarding climate change mitigation. We also collected data on the perceived influence of farmers and other persons on farming decisions. Information on farmers' adoption of climate change mitigation measures and behavioural characteristics was collected in a previous online survey. Farm characteristics were obtained from census data.