Francesca Pallotti

Dr Francesca Pallotti MSc PhD

Associate Professor in Economic Sociology

Key details

Dr Francesca Pallotti
MSc PhD

Associate Professor in Economic Sociology


Francesca Pallotti is an Associate Professor in Economic Sociology at the Networks and Urban Systems Centre, University of Greenwich (UK). She holds a PhD from the Catholic University of Rome (Italy) and a MSc from the University of Birmingham (UK). She has been postdoctoral researcher and Swiss National Science Foundation fellow at the University of Lugano (Switzerland), where she is currently an international fellow of the Social Network Analysis Research Center (SoNAR-C). She is Member of the International Network for Social Network Analysis (INSNA).

Her research draws upon a network perspective to examine how the structure of social relations develops and affects individual opportunities and outcomes. One line of research focuses on network dynamics, which includes the mechanisms underlying the creation of social networks and the forces that drive network evolution. Another line of research considers how the structure of relations affects individual behavior and outcomes. Dr Pallotti's research has examined processes of network formation, evolution and social influence among and within healthcare organizations, and most recently among physicians, and individuals affected by behavioral eating disorders.

Responsibilities within the university

  • Course Leader for International Business Strategy
  • Tutor for courses on network analysis and business organisations
  • MA/Consultancy Projects supervision
  • PhD supervision

Awards

  • Best International Paper at the Health Care Management Division, Academy of Management Annual Meeting, 2014.
  • Carolyn Dexter Award Nominee, Academy of Management Annual Meeting, 2014.
  • Best Conference Paper Award Nominee, Strategic Management Society Annual Meeting, 2009.

Recognition

Professional activities and service

Co-chair of the "Multilevel Networks" session at the 1st European Conference on Social Networks, 2014

Reviewer for the Swiss National Science Foundation, Division of Humanities and Social Sciences Reviewer for Social Networks, European Management Review, Journal of Public Health Research, Statistics in Medicine

Reviewer for Academy of Management Annual Meeting, Organization and Management Theory division

Consulting Project Manager (Sep 2012 – April 2013) at the "La Carità" Hospital, Cantonal Hospital Corporation (EOC, Ente Ospedaliero Cantonale), Locarno, Switzerland. Winner of the Excellence EFQM 5 stars by the European Foundation for Quality Management (EFQM), and the Esprix Swiss Preis for Excellence (Award date: March 7, 2014).

Professional affiliations

  • INSNA (International Network for Social Network Analysis), since 2009.
  • Academy of Management, since 2008.
  • Strategic Management Society, since 2009.

Research / Scholarly interests

Dr Pallotti's research interests lie in the area of intra- and inter-organizational networks. Her current research focuses on the dynamics of collaborative inter-organizational routines and competitive interdependence within organizational communities.

In particular, her research investigates why organizations enter into collaboration and resource exchange relationships and how these relationships affect a variety of organizational outcomes.

Key funded projects

2012 – 2015: Senior Research Associate and Project Manager

Project: "Social Influence in Dynamic Networks". European Science Foundation, European Collaborative Research Project in the Social Sciences (ECRP VI, European Collaborative Research) and Swiss National Science Foundation.

We propose a research project designed to address three related questions about the relation between interorganizational networks and organizational performance.

The first question concerns processes of social influence: How do network structures in which organizations are embedded affect organizational outcomes of economic interest? This first question is important because extant research has found evidence that economic outcomes are significantly influenced by relations that organizations develop with partners, and with actual or potential competitors. The second question concerns processes of social selection: How do organizations select their partners?

This second question is important because if networks of partners influence individual behavior, then it matter greatly who these partners might be and how they are selected. It is possible, for example, that differences in performance between organizations may be due at least in part to differential abilities to select valuable partners.

The third question arises as a logical consequence of the first two: how does similarity in individual performance affect the creation of network ties between organizations? This question is important because if similarity in performance (which is an outcome of social influence) forms the basis for change in network ties (which are antecedents of network structure) then a feedback relation exists that links social "structure" and individual "agency."

The outlined proposal must be understood as a sub-component of a broader European Collaborative Research Project in the Social Sciences (ECRP) – ECRP VI (2010) - involving six other research teams from the University of Oxford (UK), University of Groningen (The Netherlands), University of Örebro (Sweden), University of Turku (Finland), University of Ljubljana (Slovenia), and the University Autonomous of Barcelona (Spain). The global project is coordinated by Professor Tom A.B. Snijders (University of Oxford). The main objective of the overall European-level project is to explore the applicability of statistical models for the analysis of social networks to study social influence processes and to extend such models to the broadest possible variety of empirical settings.

2009 – 2012: Senior Research Associate and Project Manager

Project: "Niches, Networks and the Propensity of Organizations to Collaborate". Schweizerscher Nationalfonds zur Fõrderung (Swiss National Science Foundation)

The purpose of this project was to establish a general model to reconcile apparently contentious theoretical interpretations of the effects of similarity in resource dependence profiles on the propensity of organizations to collaborate.

 

According to the first interpretation the more two organizations depend on similar resources – i.e., the more their niches overlap - the more intense is their rivalry. Because competition erodes social relations, the lower will be the propensity of rival organizations to cooperate.

 

According to the second interpretation organizations made similar by overlapping niches will find it less costly to communicate across their boundaries and manage their joint resource dependencies by establishing - rather than severing - exchange relations with other organizations. We argue that the rapprochement of conflicting theoretical visions is possible in the context of a model that admits the presence of a non-monotonic relation between similarity in patterns of resource dependence and inter-organizational collaboration.

 

In the model that we propose the propensity of organizations to collaborate is controlled by two opposing forces - opportunity for cooperation and rivalry – that depend on niche overlap. The model makes two main assumptions. The first is that opportunities for cooperation increase with niche overlap, but at a decreasing rate. The second is that rivalry increases with niche overlap at an increasing rate. The main implication of these assumptions is a non-monotonic relation between similarity resource dependence profiles and the propensity of interdependent organizations to collaborate and exchange resources.

 

The analytical part of the project involved the estimation of stochastic models for discrete counts. Because the analysis of dyadic data poses complicated inferential problems induced by the lack of independence of the observations, statistical models for social networks were also estimated. Exponential random graph models (ERGM) were estimated on the individual yearly panels. Statistical models developed specifically for dynamic network panel data were also estimated.