Influence of Intellectual Capital Investment, Risk, Industry Membership and Corporate Governance Mechanisms on the Voluntary Disclosure of Intellectual Capital by UK Listed Companies
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
<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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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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