Guidelines for CEO‐speak: editing the language of corporate leadership
Bibliographic record
Abstract
Purpose The paper highlights the strategic importance of being alert to the power of the language and words used by CEOs in their various communications – their CEO‐speak. Design/methodology/approach The paper employs a close reading analysis of several contemporary examples of one of the most significant genres of CEO‐speak – the CEO's annual letter to stockholders. Findings Four perspectives important for understanding corporate strategy are highlighted: the importance of CEO‐speak as a linguistic marker of CEO narcissism; the revealing nature of metaphors chosen by CEOs; the potential rhetorical potency that arises from the way CEO‐speak is framed; and the significance of cultural keywords. Research limitations/implications Case examples, such as the close readings in this article, possess the strength of specific instance detail and interpretation, and the ostensible weakness arising from interpretation of small samples. But such research may provide for a reframing of conceptual perspectives and practical approaches. Practical implications The case examples and advice provided will help business executives and corporate stakeholders to monitor the quality of CEO‐speak, engage CEO‐speak more effectively for strategic purposes, and improve CEO text and leadership‐through‐language. Originality/value Readers are reminded of the power of CEO text, the benefits of subjecting it to greater scrutiny, and are provided with some practical, operational advice.
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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 | 0.015 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".