Identifying Linguistic Markers of CEO Hubris: A Machine Learning Approach

This paper explores the potential of machine learning for recognizing and analysing linguistic markers hubris in CEO speech. research is based on three assumptions: associated with potentially destructive leader behaviours; utterances are a way distinguishing between leaders who likely to exhibit such identifying at‐a‐distance using techniques provides reliable, automated scalable method identification prevention outcomes emanating from hubris. Using techniques, we analysed spoken sample hubristic CEOs compared them non‐hubristic CEOs. We found that algorithms have ability identify automatically versus speech patterns. One main implications this study building foundation future studies interested application fields other forms leadership, role language plays management organizations more generally. discuss data extraction analysis prediction CEOs’, employees’, category membership, intentions behaviours. offer recommendations how leadership can be managed curtailed effectively, thereby obviating their negative consequences.