Tone at the top, corporate irresponsibility and the Enron emails
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 it