A Bibliometric Review of a Decade of Integrated Reporting Research <sup>*</sup>
Bibliographic record
Abstract
ABSTRACT Understanding the current status and future directions of integrated reporting (IR) within today's voluntary reporting landscape is crucial for advancing accounting scholarship and practice. This study presents a comprehensive bibliometric review of IR research alongside content analysis to identify publication trends, research collaborations, leading authors, universities and countries, and emerging themes and subthemes within the field. The study analyzes 588 journal articles extracted from the Web of Science and Scopus databases, spanning 2011 to March 2024. Publications' trend analysis shows a steady growth in IR research from 2011 to 2022, followed by a significant decline in 2023. The coauthorship analysis of authors and countries reveals strong collaborations between authors from certain countries, such as South Africa, New Zealand, Italy, and Australia, with a particularly strong network between Italian researchers. In addition, the application of content analysis identifies nine unique themes in IR research: (1) International Integrated Reporting Council (IIRC) and IR Framework; (2) IR adoption and practice; (3) IR and integrated thinking; (4) IR quality; (5) IR and economic benefits; (6) IR and capital types; (7) IR audit and assurance; (8) IR versus other reporting; and (9) IR and the public sector. A detailed analysis of each cluster indicates that empirical studies largely drive the field, while classical theories such as agency, institutional, stakeholder, and legitimacy frequently underpin IR literature. Several gaps in the existing literature are highlighted, offering promising avenues for future research in the field of IR.
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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 | 0.049 | 0.171 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.205 | 0.285 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".