Managerial hubris detection: the case of Enron

Eyal Eckhaus, Zachary Sheaffer

Research output: Contribution to journalArticlepeer-review

30 Scopus citations

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.

Original languageEnglish
Pages (from-to)304-325
Number of pages22
JournalRisk Management
Volume20
Issue number4
DOIs
StatePublished - 1 Nov 2018

Keywords

  • Enron
  • Hubris
  • Leadership
  • Natural language processing
  • Risk

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