Mapping the scientific structure of organization and management of enterprises using complex networks

Alicia Olivares-Gil; Adrián Arnaiz-Rodríguez; José Miguel Ramírez-Sanz; José Luis Garrido-Labrador; Virginia Ahedo; César García-Osorio; José Ignacio Santos; José Manuel Galán

doi:10.4995/ijpme.2022.16666

Authors

Alicia Olivares-Gil Universidad de Burgos https://orcid.org/0000-0002-3378-197X
Adrián Arnaiz-Rodríguez ELLIS (European Lab. for Learning and Intelligent Systems) Unit Alicante, Universidad de Alicante https://orcid.org/0000-0001-5567-801X
José Miguel Ramírez-Sanz Universidad de Burgos https://orcid.org/0000-0003-0189-8046
José Luis Garrido-Labrador Universidad de Burgos https://orcid.org/0000-0002-3441-4223
Virginia Ahedo Universidad de Burgos https://orcid.org/0000-0002-9812-388X
César García-Osorio Universidad de Burgos https://orcid.org/0000-0002-1206-1084
José Ignacio Santos Universidad de Burgos https://orcid.org/0000-0002-6653-043X
José Manuel Galán Universidad de Burgos https://orcid.org/0000-0003-3360-7602

DOI:

https://doi.org/10.4995/ijpme.2022.16666

Keywords:

Complex networks, community detection, doctoral theses, pattern recognition, interdisciplinarity, Organization and management of enterprises

Abstract

Understanding the scientific and social structure of a discipline is a fundamental aspect for scientific evaluation processes, identifying trends and niches, and balancing the trade-off between exploitation and exploration in research. In the present contribution, the production of doctoral theses is used as a proxy to analyze the scientific structure of the knowledge area of business organization in Spain. To that end, a complex networks approach is selected, and two different networks are built: (i) the social network of co-participation in thesis examining committees and thesis supervision, and (ii) a bipartite network of theses and thesis descriptors. The former has a modular structure that is partially explained by thematic specialization in different subdisciplines. The latter serves to assess the interdisciplinary structure of the discipline, as it enables the characterization of affinity levels between fields, research poles and thematic clusters. Our results have implications for the scientific evaluation and formal definition of related fields.

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Author Biography

Alicia Olivares-Gil, Universidad de Burgos

Departamento de Ingeniería Informática

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