Influence of Intellectual Capital Investment, Risk, Industry Membership and Corporate Governance Mechanisms on the Voluntary Disclosure of Intellectual Capital by UK Listed Companies
Bibliographic record
Abstract
<p>This research examines the cross-sectional effect of intellectual capital investment, financial measures of market and company specific risk, industry membership and corporate governance on the extent of voluntary disclosure of intellectual capital (VDIC) in a sample of 443 FTSE All Share Index company annual reports for the year 2003/2004. The extent of disclosure is measured by a disclosure index (DI) based on intellectual capital (IC) attributes included in the narratives and illustrations of the annual reports. The research predicts that agency costs are mitigated by VDIC and that the benefits of signalling IC may outweigh competitive and proprietary costs that may be more prevalent in innovative and technological companies; furthermore, that effective corporate governance measures enhance VDIC particularly in those companies found to have a higher level of intangible assets (IA) in their resource base. The results suggest that companies associated with less financial risk, reduced debt, higher levels of liquidity and accompanied by growth are characterised with higher levels of VDIC. Although less significant, the results on market risk indicate a positive influence on VDIC. Furthermore, the extent of VDIC in annual reports is enhanced when large companies operating in high-tech and innovative industries are characterised by investments in employees; in contrast, companies associated with research and development processes tend to be more secretive with respect to VDIC. The results suggest that companies that are able to maintain adequate governance systems through segregation of executive and non-executive duties and to a less extent through the presence of experienced non-executive directors exhibit higher levels of disclosure.</p>
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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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".