The Role of Abnormal Tone in Board Reports in Shaping CSR Performance
Bibliographic record
Abstract
Purpose: This study examines how tone management in board reports influences corporate social responsibility (CSR) performance in emerging markets, focusing on the Tehran Stock Exchange. It addresses the underexplored qualitative aspects of CSR disclosures, particularly how abnormal tone signals transparency or concealment in sustainability reporting. Design/methodology/approach: This paper is based on a postgraduate study completed in 2022. Using a dataset of 987 firm-year observations (2016–2022), we measure abnormal tone through textual analysis of board reports and assess its impact on six CSR dimensions. The methodology combines vocabulary-based tone detection with regression analysis, controlling for firm-specific factors. Findings: The results reveal a significant negative relationship between abnormal tone and CSR performance, particularly in environmental and energy dimensions. The adverse effects persist into subsequent years, highlighting the long-term consequences of tone manipulation. Originality/value: This study contributes to the social and environmental accounting literature by analysing tone management in an emerging market context. It introduces vocabulary combinations as a novel approach to detecting nuanced tone variations, offering practical insights for regulators and firms aiming to enhance CSR transparency.
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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 | 0.008 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| 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".