Alicia Olivares-Gil¹, Adrián Arnaiz-Rodríguez¹, José Miguel Ramírez-Sanz¹, José Luis Garrido-Labrador¹, Virginia Ahedo², César García-Osorio¹ , José Ignacio Santos² and José Manuel Galán²

¹ Universidad de Burgos, Departamento de Ingeniería Informática, Escuela Politécnica Superior, Ed. A1, Avda. Cantabria s/n 09006, Spain

² Universidad de Burgos, Departamento de Ingeniería de Organización, Escuela Politécnica Superior, Ed. A1, Avda. Cantabria s/n 09006, Spain

aolivares@ubu.es, adrianar@ubu.es, jmrsanz@ubu.es, jlgarrido@ubu.es, vahedo@ubu.es, cgosorio@ubu.es, jisantos@ubu.es, jmgalan@ubu.es

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

Understanding the scientific structure is a fundamental step in identifying and evaluating scientific production [1]. Among the different options and tools available for analysis, doctoral theses are particularly interesting. Doctoral work usually entails more investment and effort than other scientific products that may have a more exceptional and opportunistic approach. This characteristic makes its analysis very relevant for establishing robust research lines and trends. Moreover, in the case of Spain, the influence of thesis supervisors in proposing the committees allows us to understand the academic structure of the different scientific fields and, at the same time, the social structure on which they are supported.

In this work, we analyze the scientific structure of doctoral theses in the knowledge area of business organization —Organización de empresas— in Spain. We use complex network analysis [2] on the TESEO database maintained by the Spanish Ministry of Education, Culture and Sports (https://www.educacion.gob.es/teseo). Previous studies in this field have shown that participation in thesis committees has a modular structure and a strongly unequal degree distribution compatible with a truncated power-law [3].

We have retrieved all the theses assigned to one or more of the nine subdisciplines in which knowledge in Organization and management of enterprises is specialized (i.e., Sales Management, Industry Studies, Manpower Management, Financial Management, Operations Research, Marketing, Optimum Production Levels, Organization of Production and Advertising) according to the UNESCO 6-digit codes [4]. The communities found on co-participation in doctoral thesis committees combined with the thematic information of each thesis have allowed us to identify the level of thematic specialization of each scientific community. The network backbone, focused on the most active scholars in the field, shows nine different communities. The average profile of each group has been calculated from the specific doctoral experience of each researcher. The level of specialization of each community has been estimated using the normalized entropy of the subdisciplines distribution. The analysis shows highly specialized academic groups in the Marketing and Advertising subdisciplines and an intermediate level in the others, with different expertise combinations. The information of this modular structure and the specialization of each community can guide the process of defining appropriate and diverse research evaluation boards.

This work also analyzes the interdisciplinary structure of Organization and management of Enterprises field. This process has been performed through the projection of descriptors of the bipartite network between theses and descriptors. Results unveil the different degrees of affinity of the subdisciplines with the field. These scientific areas are structured around two poles: one centered on instrumental and technological quantitative disciplines and the other closer to sociology and psychology domains. This map allows empirically identify niches in research and quantify the level of association between subdisciplines. This information can help to optimize faculty reallocation according to the similarity and complementarity between scientific domains.

Acknowledgments: The authors acknowledge financial support from the Spanish Ministry of Science, Innovation and Universities (HAR2017-90883-REDC and RED2018‐102518‐T), from the Junta de Castilla y León – Consejería de Educación (BDNS 425389 and BU055P20), from the Spanish State Research Agency (PID2020-118906GB-I00/AEI/10.13039/501100011033) and from NVIDIA Corporation and its donation of the TITAN Xp GPUs that facilitated this research. Alicia Olivares-Gil and José Luis Garrido-Labrador were supported by the predoctoral grant awarded by the Universidad de Burgos. In addition, the authors would like to thank Dr. Manzanedo, Dra. Saiz-Bárcena, Dr. Solé Parellada, Dr. Izquierdo and Dr. del Olmo for their insightful help to improve the manuscript.

References

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