Opening the black box: Uncovering the leader trait paradigm through machine learning

Abstract

Understanding the traits that define a leader is a perennial quest. An ongoing debate surrounds the complexity required to unravel the leader trait paradigm. With the advancement of machine learning, scholars are now better equipped to model leadership as an outcome of complex patterns in traits. However, interpreting those models is often harder. In this paper, we guide researchers in the application of machine learning techniques to uncover complex relationships. Specifically, we demonstrate how applying machine learning can help to assess the complexity of a relationship and show techniques that help interpret the outcomes of “black box” machine learning algorithms. While demonstrating techniques to uncover complex relationships, we are using the Big Five Inventory and need for cognition to predict leadership role occupancy. Among our sample (n = 3385), we find that the leader trait paradigm can benefit from modeling complexity beyond linear effects and generate several interpretable results.