Tone at the top, corporate irresponsibility and the Enron emails
Bibliographic record
Abstract
Purpose This study aims to examine whether senior Enron executive emails celebrated, or at least left a space for, corporate irresponsibility. Engaging with prior organizational-focused research, we investigate how corporate emails sent by senior executives help constitute Enron by communicating to employees senior management’s stance about important topics and social characters. Design/methodology/approach The study analyzes the 527,356 sentences contained in 144,228 emails sent by Enron senior executives and other employees in the three-year period (1999–2001) before the company’s collapse. Sentences are used as the base-level speech unit because we are interested in identifying the tone and emotions expressed about specific topics and stakeholders. Tone is measured using Loughran and McDonald’s (2016) financial dictionary approach, and emotion is measured using Mohammad and Turney’s (2013) NRC word-emotion lexicon. Least Absolute Shrinkage and Selection Operator (LASSO) regressions are used to explore the determinants of senior management tone and emotions. Findings The analysis illustrates that while both senior executives and other employees utilized email to help accomplish task-related activities, they employed different evaluative tones to talk about key topics and stakeholders. Also important is what is left unsaid, with a “spiral of silence” emanating from senior management that created a space for corporate irresponsibility. Originality/value Combining advanced computerized textual analysis with qualitative techniques, we analyze a unique dataset to explore micro details involved in using email to communicate a tone at the top. The findings illustrate how what is said or not said by senior management contributes to the constitution of an organization.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".