The Impact of Corporate Reporting Quality on Sustainable Growth Through Integrated Reporting Lens in Thai Listed Companies
Bibliographic record
Abstract
The study investigates the relationship between corporate reporting quality, viewed through an integrated reporting perspective, and sustainable growth among Thai-listed companies during the period from 2019 to 2022. Utilizing a sample of 59 SET50 companies and analyzing 232 annual reports, an Integrated Reporting Quality Index (IRQI) was developed to assess reporting quality across three principal components—capitals, guiding principles, and content elements—as well as their respective sub-components, enabling comprehensive evaluation at both macro and micro levels. Although the component-level analysis identified no significant relationships with sustainable growth, the sub-component analysis revealed critical insights. Information connectivity, conciseness, and business model disclosure demonstrated positive associations with sustainable growth, whereas strategic focus exhibited a negative relationship. These findings contribute to the extension of stakeholders and signaling theories within emerging market contexts, emphasizing the importance of effective communication mechanisms over the sheer volume of disclosures. The study further documents substantial improvements in reporting quality following the implementation of the One Report framework, suggesting that well-designed regulatory interventions can elevate corporate disclosure standards. The results offer valuable implications for managers, regulators, and investors, underscoring that fostering effective information connectivity, conciseness, and clear articulation of business models contributes more significantly to sustainable growth than simply increasing the quantity of disclosed information.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.003 |
| 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; 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".