Determinants of Companies that Disclose High-Quality Integrated Reports
Bibliographic record
Abstract
Integrated reporting is becoming increasingly popular. The focus of this study was to assess the overall integrated reporting quality of global companies and find determinants of high-level integrated reporting. Qualitative text analysis was performed on the 2017 integrated reports of 110 global organizations to determine in what way companies report on specific topics related to the six capitals: social and relationship, human, intellectual, manufacturing, natural, and financial. Using a novel assessment technique, scores were then assigned according to the details provided in the integrated reports on the various topics. This was done for each form of capital, and the total integrated score was subsequently calculated as the average between all the capital scores. Finally, a regression analysis was performed to determine the characteristics of high-quality integrated reporters. The results of univariate analysis and two-stage least squares instrumental variable (2SLS) regression indicate that companies of a larger size with a higher female board ratio and listing in the International Integrated Reporting Committee (IIRC) examples database are more likely to publish a higher quality integrated report. The results imply that these variables are the main disclosure drivers. However, a significant negative correlation was found between integrated report quality and the variables related to female executive ratio, external board member ratio, profitability, leverage, and previous report experience, as well as report length. No significant association was found between the location and industry group and report quality. The empirical evidence of this study shows that even though integrated reporting has become more common overall, the comparability and quality of the reports still remain low.
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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 | 0.012 | 0.087 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".