The effect of institutional setting on attributional content in management commentary reports
Bibliographic record
Abstract
We study the effect of expected regulatory and litigation costs embedded in a country’s institutional environment on the explanatory content of management commentary reports. Using a behavioural accountability lens, we argue that regulatory control and expected litigation risk affect the attributional framing of financial performance. We also investigate whether differential attributional properties have economic relevance by considering the relationship between content profiles and analyst forecast dispersion. We include 173 listed firms from four countries (USA, Canada, United Kingdom and Australia). Consistent with behavioural accountability theory, we find significant country differences in dominant attributional profiles. Compared to their counterparts in the UK and Australia, firms from the USA and Canada are generally less assertive and less defensive in explicit causal framing. They are also more extensive and formal in their explanations, relying more heavily on accounting-technical language. These tendencies are more pronounced in the USA, where the aggregate of private and public enforcement is greatest. Moreover, we establish that causal defensiveness and attributional extensiveness are negatively associated with analyst forecast dispersion, while level of causal assertiveness and formality are not.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.024 | 0.268 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".