Decoding the Pygmalion language of top CEOs: the communication of high positive expectations in CEO letters
Bibliographic record
Abstract
Purpose Effective communication of high positive expectations regarding individuals, the organization and the favorability of the environment in which organizations operate is pivotal due to its significant impact on multiple positive organizational outcomes. Drawing on prominent American CEOs' shareholder letters as a primary data source, this study seeks to deepen our understanding of how Pygmalion language can be effectively applied, and how specific linguistic devices may be used to reinforce expectancy messages. Design/methodology/approach To identify and analyze the occurrence and strength of the Pygmalion language and the use of linguistic devices in the CEOs expectancy messages, a quantitative content analysis with some quantitative components were employed. Findings The findings suggest that top management make extensive use of Pygmalion language in their communication to develop their organizations. CEOs use of linguistic devices to bolster the Pygmalion messages in the CEO letters is also considerable. The combined but various use of axioms, performatives, metaphors, slogans and emotion-laden words also suggest a merging of Pygmalion and charismatic leadership behaviors. Practical implications Findings provide insights into how top CEOs communicate high positive expectations, enhanced by strategic use of linguistic devices. This may advise other practitioners regarding how to effectively communicate high expectations to improve performance in their organizations. Originality/value To the authors’ knowledge, this is the first qualitative study on Pygmalion language, analyzing how top managers communicate high positive expectations to their audiences in real-world business settings.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".