CEO personality and language use in CSR reporting
Bibliographic record
Abstract
Abstract We explore the relationship between chief executive officer (CEO) personality traits and corporate social responsibility (CSR) reporting. Upper echelons theory indicates that the values, experiences, and personalities of top organizational managers influence their organization's strategic decisions and effectiveness. We utilize IBM Watson Personality Insights software to infer CEOs’ personality traits based on their responses to questions raised by analysts during year‐end conference calls; we obtain CEOs’ Big Five personality traits—openness, conscientiousness, extraversion, agreeableness, and neuroticism—from which we compute a measure of their risk tolerance. Using a longitudinal dataset of Standard and Poor's 500 firms for 2008–2015, we document that high CEO risk tolerance is related to lower CSR report readability and smaller CSR disclosure volume. This finding indicates that executives who are comfortable with greater risk are more willing to supply stakeholders with reports that are shorter and require greater effort to understand. Exploration of the association between CEO Big Five personality traits and CSR report readability and disclosure volume allows key stakeholders to better comprehend CSR disclosures and connotations thereof. Overall, our results contribute to the debate on how CEO personality traits affect organizations’ CSR disclosure reporting strategies, and support upper echelons theory in the CSR setting.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".