Impression Management in Corporate Social Responsibility Reporting: An Analysis of Chief Executive Officer Letters in the Oil and Gas Sector
Bibliographic record
Abstract
ABSTRACT This study aims to investigate the external and internal determinants that lead Chief Executive Officers (CEOs) of oil and gas companies to obfuscate and manipulate Corporate Social Responsibility (CSR) reporting. The analysis focuses on CEO letters from CSR and integrated reports of 24 companies, between 2008 and 2021. A total of 336 company‐year observations were analyzed. Quantitative methods based on readability indexes, descriptive, inferential, and regression analysis are adopted. Macroeconomic conditions, CSR reporting frameworks, and cultural backgrounds determine the readability of CEO letters. The length of the letters, company's size, CEOs' age, and female representation on boards also influenced CEOs to engage in impression management (IM). The findings allow stakeholders to have a more truthful view of the impact of IM on CSR reporting. This study highlights an unstudied perspective on the impact of external and internal determinants on the readability of CSR reporting in an environmentally sensitive sector.
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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 | 0.010 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".