Executive Religiosity and Disclosure Tone Ambiguity of Annual Reports
Bibliographic record
Abstract
This paper examines the effect of C-suite executive religiosity on the disclosure tone ambiguity of corporate annual reports. The paper utilizes executive-level religiosity, disclosure tone, and financial data from a sample of 2515 publicly listed U.S. corporations. It applies fixed-effect regression analysis to show that the presence of religious executives within the C-suite team reduces the disclosure tone ambiguity of annual reports, as evidenced by a reduction in the number of negative and uncertain words within corporate annual reports. Subsample analyses show that religious CEOs and CFOs in the C-suite primarily drive the main findings, which is consistent with their heightened control over corporate annual report preparation processes post-SOX. The main finding holds across multiple robustness tests and suggests that the individual religiosity of C-suite executives can be an important determinant of a company’s disclosure tone-related choices. By utilizing the measure of executive-level religiosity, this study directly addresses recent calls for further research to examine additional personal and psychological factors beyond executive-level narcissism and political ideology that can influence top management personnel’s corporate disclosure tone-related choices. This study contributes to the literature by examining the influence of individual executive-level religiosity on the tonal sentiment of corporate communications, as represented by corporate annual reports.
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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 | 0.002 | 0.019 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".