The Effect of Intellectual Capital on Innovation and Performance of Companies Listed on B3 S/A
Bibliographic record
Abstract
The research aims to investigate the effect of intellectual capital on innovation and performance of companies listed on B3 S/A. To this end, statistical results were analyzed for three hypotheses: IC positively influences performance (H1); IC positively influences innovation (H2); and innovation positively influences performance (H3). Based on the PLS-SEM method and the Robust Path Analysis technique, with the aid of the WARPPLS software (version 3.0), the data of 142 companies listed on B3 S/A, in the period from 2010 to 2020, in relation to the variables: degree of intagibility (intellectual capital); intangible (innovation); Tobon’s Q, Ebitda Margin, ROE and revenue growth (performance); sector, size, age and year (control variables) were tested. The adjustment results confirm the quality and explanatory power of the model, confirming the three formulated hypotheses. Thus, the analyses demonstrate the contribution of IC to innovation, as well as to the organizational performance of companies.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".