Financial statement informativeness and intellectual capital disclosure
Bibliographic record
Abstract
Purpose – The purpose of this paper is to analyse the relationship between financial statement informativeness (FSI) and intellectual capital disclosure (ICD). Design/methodology/approach – While FSI was measured as the explanatory power of financial information in explaining market value, ICD was collected through content analysis of annual reports. A sample of 126 US companies, divided into two groups – high-tech and low-tech companies – were used in this study. Empirical analysis was carried out using the Poisson regression method. Findings – The results show a negative (substitutive) relationship between FSI and ICD, especially in high-tech companies. This indicates that companies with low FSI disclose more information about their IC in annual reports. Practical implications – This study confirms the role of voluntary ICD as a solution towards mitigating the problem of the distortion of financial information due to the lack of accounting recognition of IC as an asset in the financial statements. Originality/value – This is the first empirical study to analyse the relationship between FSI and ICD. Therefore, it serves as feedback to the regulators and standard-setters that recently published recommendations on voluntarily disclosing IC.
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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 | 0.009 | 0.112 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".