The Disclosure Quality Issues of Integrated Report: A Systematic Literature Review
Bibliographic record
Abstract
While systematic literature review has contributed considerably to the emerging field of integrated reporting (IR), they have made limited contributions to IR quality. Assessing the quality of integrated reports is vital in showcasing the transparency and reliability of corporate information. Hence, this study reviewed the existing literature to identify the disclosure quality issues of integrated reports prepared by its adopters. Guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), this study systematically reviews 36 articles extracted from Scopus and Web of Science (WoS). Following thematic analysis, we identify four disclosure quality issues: adherence to the reporting framework, environmental, social, and governance (ESG) disclosures, characteristics of reported information, and reporting form. The intense review reveals that the level of IR disclosure reported in previous studies is low. IR adopters principally ignore the International Integrated Reporting Council (IIRC) framework's reporting requirements and the importance of a good reporting structure. The results of this study indicate the importance of examining reasons for the companies to hold some of the information while notifying the IIRC to reconsider their reporting framework or formulate an appropriate plan that assists adopters in preparing high-quality reports. Keywords: Integrated reporting quality; disclosure quality issues; integrated reports; systematic literature review; IIRC framework
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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 | 0.126 | 0.381 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.034 | 0.027 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| 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".