The triangulation of ethical leader signals using qualitative, experimental, and data science methods

To advance ethical leadership using signaling theory, the current work presents a mixture of inductive and deductive studies. Using a constant comparative analysis method, Study 1 involved coding CEO letters to share-holders (n = 10,919 sentences). Eight verbal ethical leader signals (ELSs) emerged and were associated with emotions (e.g., righteous anger, pride). In a set of preregistered experiments, ELSs were found to lead to eval-uations of ethical leadership (Study 2: n = 264; Cohen's d = 0.26). Study 3 illustrated that ELSs led to a reduc-tion in financial theft (n = 434; Cohen's d = 0.20). Study 4 showed that ELSs led to an improvement in performance (n = 434; Cohen's d = 0.18) but had little effect on extra role behavior (Cohen's d = 0.06). Finally, in Study 5 a machine learning algorithm, DeepEthics, was created to automatically score text (ROC =. 84; r = 0.85 between human and algorithm scores), such as emails and meeting transcripts, for ELSs in future research. Recommendations for theory and practice are discussed.