Unlocking corporate sustainability: the synergistic effects of integrated reporting and ownership concentration
Bibliographic record
Abstract
Purpose This study aims to explore the potential effects of Integrated Reporting Quality (IRQ) on firm value and sustainability. Specifically, it investigates whether higher IRQ significantly affects various financial and non-financial dimensions of firm performance (FP). Additionally, it examines the moderating roles of ownership concentration (OC) and the mandatory adoption of Integrated Reporting (IR) in the IRQ–FP relationship. Design/methodology/approach The study draws on 108 integrated reports from European and South African companies over the 2017–2019 period. Using the International Integrated Reporting Council (IIRC) framework, the paper applies content analysis to assess 5,724 disclosure items and constructs a novel comprehensive IRQ metric. Findings The findings document a positive and significant effect of IRQ on firm value and sustainability. Furthermore, the results indicate that the impact of IRQ is considerably stronger under conditions of higher OC, particularly within a mandatory IR regime. Originality/value This research contributes to the literature by empirically examining the consequences of IRQ for sustainability performance. In addition, the introduction of a novel and reliable IRQ metric strengthens the rigor and credibility of the analysis. To the best of the authors’ knowledge, this is the first paper to investigate how OC moderates the link between IRQ and FP. The study also explores the differential effects of IRQ across diverse geographical and legal contexts and highlights the critical role of IRQ, particularly under mandatory IR, in driving firm value and sustainability.
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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 | 0.017 | 0.091 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.005 |
| 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".