Impression Management Tactics in the Chairperson’s Statement: A Systematic Literature Review and Avenues for Future Research
Bibliographic record
Abstract
The chairperson’s statement (CS) has evolved into a key component of corporate reporting, offering an authoritative, high-level summary of a company’s activities, initiatives, operations, financial performance, and achievements over the preceding financial year, along with insights into future outlooks. Recognised for its informative value, the CS is consistently ranked by stakeholders as the most read and most influential section of the integrated report. Despite its importance, the CS is also a platform where corporate management often engages in impression management (IM) to portray a biased and overly positive image of the company. This study conducted a systematic literature review to examine the IM tactics employed within the CS. Based on the findings, an integrative conceptual framework was developed. Identified IM tactics include readability, textual characteristics, the influence of culture, legal systems and capital markets, paratext and intertextuality, the tone of language, forward-looking statements, retrospective sense-making, ambiguous language, the use of photographs and graphs, impersonalisation and evaluative language, and self-serving attributions. The results highlight that the study of IM strategies in CSs represents a rich and relevant research domain that warrants deeper exploration. Given its qualitative complexity and underexplored dimensions, this area offers several promising avenues for future investigation.
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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 | 0.052 | 0.170 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.015 | 0.015 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".