Investigating the Relationship Between Leadership Style and Nurse Motivation in Healthcare Organizations

Posted: Dec 22, 2023

• Eva Barrett
• Declan Boyd
• Leighton Cruz

Abstract

This research presents a novel computational framework for analyzing the complex relationship between leadership styles and nurse motivation in healthcare organizations using artificial intelligence and natural language processing techniques. Traditional studies in healthcare leadership have primarily relied on survey-based methodologies and qualitative assessments, which often fail to capture the dynamic, multi-dimensional nature of leadership-motive interactions. Our approach introduces a hybrid methodology combining sentiment analysis of nurse feedback, behavioral pattern recognition from electronic communication logs, and machine learning classification of leadership effectiveness. We developed a unique dataset comprising over 15,000 anonymized nurse-leader interactions across three major healthcare systems, which we processed using our custom-designed Leadership-Motivation Interaction Matrix (LMIM). The results reveal previously undocumented nonlinear relationships between specific leadership behaviors and motivational outcomes, including the identification of 'motivational tipping points' where small changes in leadership approach produce disproportionately large effects on nurse engagement. Our computational model achieved 89.7