This article addresses the orchestration of scientific communities, which encompass a variety of competing and/or collaborating autonomous actors – mainly universities and researchers – which aim at developing theory. Scientific communities – which are part of more comprehensive scientific ecosystems, comprising also public and private organizations, civil society, etc. – are a very interesting subject to be studied because some of their features – specifically data building and sharing – are increasingly assuming importance for organizations that post-COVID will have to cope with the ‘new normal’, which requires them to experiment on the basis of collectively gathered data in order to accelerate their learning “on the fly”. Specifically, business ecosystems challenged by the ‘new normal’ can leverage on the experience that scientific ecosystems have always had regarding data building and sharing. Considering conferences as decentralized orchestrating moments capable of pulling together dispersed resources/capabilities by ensuring knowledge mobility, and building on a social network analysis of the CINet conferences between 2011 and 2019, we aim at understanding the characteristics of the CINet scientific ecosystem in order to infer how well connected and close its researchers are and how effectively information is shared among them.
Scientific Communities and Decentralized Orchestration: A Social Network Analysis of The CINet Conferences
Benevento E.;Pellegrini L.;
2021-01-01
Abstract
This article addresses the orchestration of scientific communities, which encompass a variety of competing and/or collaborating autonomous actors – mainly universities and researchers – which aim at developing theory. Scientific communities – which are part of more comprehensive scientific ecosystems, comprising also public and private organizations, civil society, etc. – are a very interesting subject to be studied because some of their features – specifically data building and sharing – are increasingly assuming importance for organizations that post-COVID will have to cope with the ‘new normal’, which requires them to experiment on the basis of collectively gathered data in order to accelerate their learning “on the fly”. Specifically, business ecosystems challenged by the ‘new normal’ can leverage on the experience that scientific ecosystems have always had regarding data building and sharing. Considering conferences as decentralized orchestrating moments capable of pulling together dispersed resources/capabilities by ensuring knowledge mobility, and building on a social network analysis of the CINet conferences between 2011 and 2019, we aim at understanding the characteristics of the CINet scientific ecosystem in order to infer how well connected and close its researchers are and how effectively information is shared among them.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
