PhD in Circular Economy

Department

Department of Economics, Business, Mathematics and Statistics

Main research activities/topics/projects

Scientific collaboration is considered an important driver of research progress and innovation in science. It is a mix of informal mechanisms (e.g., advice, face-to-face contacts, exchange of personal knowledge) and formal activities (e.g., writing papers, participating in research projects). This project aims to contribute to the debate on scientific collaboration networks, their evolution, their impact on scholars' performance, and the effect of individual characteristics (gender, academic rank, age) on the scholars' network embeddedness. We have a rich dataset on both formal and informal relationships and the aim is to propose novel methods to properly account for the nature of the collaboration data, which is expressed as weighted longitudinal social networks (WLN). The core methodological proposal consists of developing exploratory methods and modelling strategy suitable for evolving weighted networks. In particular: Extension of visualization methods and partition algorithms for WLN; Modeling the evolution of complete and ego-centered weighted collaboration networks; hierarchical models for collaboration ego-networks and academic performance. The project will provide an accurate picture of scientific collaboration in Italy (and the international openness of the Italian Academic community), allowing the definition of the collaboration strategies of academic researchers. It will also contribute to several University policies and actions aligned with some of the missions designed in the Piano Nazionale di Ripresa e Resilienza (PNRR). A relevant part of the project will be an international comparison of the Italian data and international data (Brazilian or in general Latin America) in terms of topology and evolution of the collaboration networks for comparable scientific disciplines or individual institutions.

Working language

English

Special entry requirements

A good knowledge of R programming language is required. Basic knowledge of social network analysis and statistical modelling.

Duration in months (min-max)

PhD sandwich: 3-6