Effect of Intellectual Capital on Firms¡¯ Competitive Advantage Condition: An Empirical Investigation in India
Bibliographic record
Abstract
This paper aims to model the effect of intellectual capital on firms¡¯ competitive advantage condition across selected Indian industries. Using a panel dataset consisting of 146 Indian companies listed in the Bombay Stock Exchange (BSE), spanning across 7 industries during 2003 to 2012. The study suggests that the firms¡¯ competitive advantage condition is relatively better explained by some of the individual intellectual capital components rather than by the Public¡¯s Composite Value Added Intellectual Capital Coefficient (VAIC) measure. Physical and financial capital efficiency (VACA) statistically determines the firms¡¯ competitive advantage condition irrespective of the industry segments. However, along with VACA, human capital efficiency (HCVA) is also observed to be a significant determinant of firms¡¯ competitive advantage conditions for automobiles, consumer goods, health and pharmaceuticals and information technology industries. The result extends the understanding of how VAIC and other associated components determine competitive advantage condition of firms¡¯ across industries in India. To the best of authors' knowledge, for the first time the firms¡¯ competitive advantage condition is modeled in a VAIC framework.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".