Financial statement informativeness and intellectual capital disclosure
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it