Mapping Neural Networks of Ideal and Actual Leaders: A Visual Representation of Implicit Leadership Theories
Graham H. Lowman
Overview
Leadership is critical to organizational success. However, despite generally agreeing on its importance, people conceptualize and perceive leadership qualities differently. Often, these differences are based on implicit leadership theories (ILTs), or a list of attributes by which they identify leaders; for example, intelligence. Our novel method analyzes ILTs from a neural network perspective, representing the attributes that comprise ILTs as interconnected nodes. It is designed to identify the central nodes, which are the attributes that most people prioritize when describing their ideal or an actual leader. Early findings from a U.S. sample suggest that differences exist for men and women when thinking about ideal leaders. For example, men associate strong leaders with being hard working, while women associate strong leaders with being knowledgeable. When asked to evaluate actual leaders, differences were found in the traits of leaders men and women associate with sincerity and dedication, as examples. While still in its early stages, this method provides a theoretically grounded approach to exploring how people conceptualize and perceive leaders.
30 | Summer Research Fellowship
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