Managerial hubris detection: the case of Enron
Eyal Eckhaus () and
Zachary Sheaffer ()
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Eyal Eckhaus: Ariel University
Zachary Sheaffer: Ariel University
Risk Management, 2018, vol. 20, issue 4, 304-325
Abstract Hubris is a known risk for leadership failure. We show that hubristic tendencies can be detected semantically ex-ante in textual reports, and offer a novel methodology aimed at detecting real-time hubristic propensities. The methodology employs text mining based on natural language processing (NLP) on Enron email corpus. NLP can capture information about employees and predict change patterns. Employing NLP real-time mechanism, Enron executives’ hubristic tendencies were detected. Findings indicate that hubristic expressions amongst senior executives are significantly more frequent than amongst their non-senior counterparts, and that the frequency of hubristic expressions increases the closer one gets to Enron’s collapse. Whilst both Enron’s CEO’s were hubristic, we found Skilling to be typified with severer hubris. Our study is the first to employ NLP real-time analytical process to detect the hubris disposition. Predicated on Enron’s case study, we demonstrate the methodology’s strengths, notably immediate recognition of accumulated symptoms and prevalence.
Keywords: Hubris; Leadership; Enron; Natural language processing; Risk (search for similar items in EconPapers)
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