Intellectual capital and Tobin’s Q as measures of bank performance
Bibliographic record
Abstract
This paper aims at examining the effect of the Value Added by Intellectual Capital (VAIC) in terms of its three components: capital employed efficiency, human capital efficiency, and structure capital efficiency on the financial performance of commercial banks listed on the Amman Stock Exchange for the period 2010–2018.Value Added of Intellectual Capital (VAIC) model was used to measure the intellectual capital while Tobin’s Q ratio was used as an indicator of bank financial performance. The study has used parametric techniques like multiple linear regression and correlation coefficient, and other statistical methods to investigate its hypothesis. It was found that only human capital efficiency and capital employed efficiency had impacts on the banks’ financial performance. These results emphasize the importance of using the VAIC model to evaluate the financial performance of these banks, as well as encourage banks to make further investments in intellectual capital’s components, and concentrate on human resources to build up their knowledge, skills and capabilities, because of their greatest role in value creation.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".