The Impact of Intellectual Capital and Ownership Structure on Firm Performance
Bibliographic record
Abstract
Even though several studies have been done on intellectual capital, ownership structure, and firm performance, their status has remained uncertain in developing countries like Malaysia. Prior studies have generally focused on a single industry and overlooked the input of all Malaysian non-financial firms. This study investigates the impact of intellectual capital, its components, and ownership structure on firm performance. This study employs a balanced panel data examination for the data of 409 non-financial firms from 11 sectors listed on Bursa, Malaysia for five years (2016–2020). The modified value-added intellectual coefficient model was applied to examine the effect of IC efficiency on firm performance. The empirical findings revealed that IC efficiency, human capital efficiency, structural capital efficiency, capital employed efficiency, and relational capital efficiency are positively and significantly related to firm performance. However, physical and structural capital is the most substantial element of intellectual capital efficiency in augmenting profitability. In addition, government and foreign ownership positively affect firm performance. The research will help managers, policymakers, and investors understand how IC investments increase performance and make prudent investment choices in government and foreign ownership firms.
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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 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".