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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.126 | 0.654 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.617 | 0.910 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".