Spotting Leaders in Organizations with Graph Convolutional Networks, Explainable Artificial Intelligence, and Automated Machine Learning
Over the past few decades, the study of leadership theory has expanded across various disciplines, delving into the intricacies of human behavior and defining the roles of individuals within organizations. Its primary objective is to identify leaders who play significant roles in the communication f...
- Autores:
-
Xie, Yunbo
Meisel, Jose D.
Meisel, Carlos A.
Betancourt, Juan Jose
Yan, Jianqi
Bugiolacchi, Roberto
- Tipo de recurso:
- Article of investigation
- Fecha de publicación:
- 2024
- Institución:
- Universidad de Ibagué
- Repositorio:
- Repositorio Universidad de Ibagué
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.unibague.edu.co:20.500.12313/5911
- Acceso en línea:
- https://doi.org/10.3390/ app14209461
https://hdl.handle.net/20.500.12313/5911
https://www.mdpi.com/2076-3417/14/20/9461
- Palabra clave:
- Redes Convolucionales de Grafos
Inteligencia Artificial Explicable
Aprendizaje Automático Automatizado
Artificial intelligence
Contrastive Learning
Economic and social effects
Federated learning
Graph embeddings
- Rights
- openAccess
- License
- © 2024 by the authors.
| Summary: | Over the past few decades, the study of leadership theory has expanded across various disciplines, delving into the intricacies of human behavior and defining the roles of individuals within organizations. Its primary objective is to identify leaders who play significant roles in the communication flow. In addition, behavioral theory posits that leaders can be distinguished based on their daily conduct, while social network analysis provides valuable insights into behavioral patterns. Our study investigates five and six types of social networks frequently observed in different organizations. This study is conducted using datasets we collected from an IT company and public datasets collected from a manufacturing company for the thorough evaluation of prediction performance. We leverage PageRank and effective word embedding techniques to obtain novel features. State-of-the-art performance is obtained using various statistical machine learning methods, graph convolutional networks (GCN), automated machine learning (AutoML), and explainable artificial intelligence (XAI). More specifically, our approach can achieve state-of-the-art performance with an accuracy close to (Formula presented.) for leaders identification with data from projects of different types. This investigation contributes to the establishment of sustainable leadership practices by aiding organizations in retaining their leadership talent. |
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