Opening Fields: A Methodological Contribution to the Identification of Heterogeneous Actors in Unbounded Relational Orders

Mohamed  Benabdelkrim; Clément  Levallois; Jean  Savinien; Céline  Robardet

doi:10.37725/mgmt.v23.4245

Mohamed Benabdelkrim Quant Research Center, Emlyon Business School, Lyon, France
Clément Levallois Quant Research Center, EM Lyon Business School, Lyon, France
Jean Savinien Quant Research Center, EM Lyon Business School, Lyon, France
Céline Robardet University of Lyon, INSA Lyon, CNRS, LIRIS UMR5205, Lyon, France

DOI: https://doi.org/10.37725/mgmt.v23.4245

Keywords: Fields, Methodology, Curation, Classification, Online Social Networks

Abstract

Institutional scholarship studies how individuals coexist and interact with social structures. Organizations and inter-organizational relations within industries are a central focus of these studies. Hence, empirical research has so far largely relied on the observation of individual actors identified by their organizational attributes, and organizations identified by their industry characteristics. The flourishing of new types of social structures has sent an invitation to observe a broader range of actors beyond organizations stricto sensu, and to define the arena of interest beyond the boundaries of industry membership. However, in practice, these remain a favorite starting point of empirical investigations. In this article, we present a new method for the study of organizational fields that facilitates the identification of a large number and varied types of actors in a given field, provides a characterization of the relational structure of the field, and offers a content analysis on different sub-regions of the field. We test the method by replicating a previous study in the field of ‘social impact of nonprofits’, and show how it can contribute to operationalize mechanisms at play in the field. We conclude by noting that the principles of this method can extend beyond the dataset it is originally built on and facilitate a comparative approach to the study of fields. This contribution should enhance the value of the field as a theoretical construct by extending its operational reach.

Manuscript accepted by Thomas Roulet

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Author Biographies

Mohamed Benabdelkrim, Quant Research Center, Emlyon Business School, Lyon, France

Mohamed Benabdelkrim is a doctoral student in data mining and network analysis. He is associated with the Data Mining and Machine Learning (DM2L) team in the computer science laboratory (LIRIS) at INSA Lyon. His research focuses on algorithms and methods for community detection in large temporal networks. He also works on possible applications of such methods on social networks for management and marketing purposes. Mohamed graduated in 2017 from INSA Toulouse with an engineering degree in applied mathematics and a specialization in statistics and machine learning.

Clément Levallois, Quant Research Center, EM Lyon Business School, Lyon, France

Clément Levallois is Associate Professor at emlyon business school. He leverages data intensive methodologies to investigate how different communities form and interact, in science and in society at large. As a Chaired implid professor in data valuation since 2018, he conducts a research project on how to measure the value of data from an accounting and financial perspective.

Jean Savinien, Quant Research Center, EM Lyon Business School, Lyon, France

Jean Savinien is Associate Professor of Data Science at emlyon business school in Lyon, France, and Associate Professor of Mathematics at the University of Lorraine, in Metz, France. His research focuses on statistical learning and network science.

Céline Robardet, University of Lyon, INSA Lyon, CNRS, LIRIS UMR5205, Lyon, France

Céline Robardet is a full professor at the National Institute of Applied Science in Lyon (France), member of the Laboratoire d’InfoRmatique en Image et Systèmes d’information (LIRIS, UMR 5205 CNRS) and coordinator of the Data Mining and Machine Learning team. She is particularly interested in: clustering analysis, pattern extraction under constraints and complex dynamic network analysis.

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