Researchers at the Indiana University Kelley School of Business have pioneered a new method to detect depression in CEOs using machine learning algorithms that analyse vocal acoustic features from conference call recordings. Published in the Journal of Accounting Research, this method presents a groundbreaking way to address a mental health concern that frequently goes unnoticed within the demanding environment of executive leadership.

The study delves into the relationship between CEO depression and career metrics such as compensation and incentives. The results reveal that CEOs exhibiting higher levels of depression often receive larger compensation packages, with a significant portion of this compensation tied to performance metrics. Furthermore, depression among CEOs correlates with an increased likelihood of their departure being tied to performance outcomes. This pattern indicates that CEOs suffering from depression are more sensitive to negative feedback, while their response to positive feedback is notably subdued.

Assistant Professor Nargess Golshan highlighted the importance of further research due to the prevalence of depression among executives. The objective is to understand better the factors contributing to depression, its impact on business decisions, and practical strategies for managing mental health within leadership roles. Such studies are essential for developing a more comprehensive approach to tackling mental health issues in high-stress professional settings.

More information: Nargess Golshan et al, Silent Suffering: Using Machine Learning to Measure CEO Depression, Journal of Accounting Research. DOI: 10.1111/1475-679X.12590

Journal information: Journal of Accounting Research Provided by Wiley