In this chapter Authors apply the techniques of the multilevel exponential random graph model (MERGM) to study the structural configuration of cross-level interactions within two Third Sector inter-organizational networks operating in Southern Italy. The multilevel models in the context of the proposed research, have allowed to test hypotheses on how and how much the presence of links to the lower-lever of social actors (such as organizations) depends on the links between actors at higher levels (such as those involving organizations and their activities). The results of the elaborations have led to affirm that the system of interdependencies that is created through participation in social interventions at the community level, strengthens collaborative networks making the outcomes more effective in terms of welfare for the territorial communities served.

A structural analysis proposal for inter-organizational networks: doing a multilevel analysis through Multilevel ERGMs

Irene Psaroudakis
;
2021-01-01

Abstract

In this chapter Authors apply the techniques of the multilevel exponential random graph model (MERGM) to study the structural configuration of cross-level interactions within two Third Sector inter-organizational networks operating in Southern Italy. The multilevel models in the context of the proposed research, have allowed to test hypotheses on how and how much the presence of links to the lower-lever of social actors (such as organizations) depends on the links between actors at higher levels (such as those involving organizations and their activities). The results of the elaborations have led to affirm that the system of interdependencies that is created through participation in social interventions at the community level, strengthens collaborative networks making the outcomes more effective in terms of welfare for the territorial communities served.
2021
Psaroudakis, Irene; Riccardo, Antonietta
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11568/1139324
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