Predicting Leadership Flexibility Using Supervised Learning Techniques

Kusum Lata and Naval Garg ()
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Kusum Lata: Delhi Technological University
Naval Garg: Delhi Technological University

Global Journal of Flexible Systems Management, 2025, vol. 26, issue 2, No 3, 295-310

Abstract: Abstract The study aims to develop and validate a flexible leadership prediction (FLP) model using supervised learning techniques. The supervised learning techniques including four machine learning (ML) (Naïve Bayes, decision tree, logistic regression, and multilayer perceptron) and four ensemble learning (EL) (Random Forest (RanF), BootStrap Aggregation (Bagg.), AdaBoost (AdaB), and LogitBoost (Lboost)) techniques were employed to develop the prediction models. Also, tenfold cross-validation method was used to validate the flexible leadership prediction model. The results suggested that the model developed using the EL techniques outperformed ML-based prediction models. Particularly, Lboost and RanF emerged as the best techniques for developing the FLP model.

Keywords: Flexible leadership; Supervised learning; Machine learning; Prediction model (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s40171-025-00439-x

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