The impact of intellectual capital on firm performance: Evidence from Tehran Stock Exchange
Bibliographic record
Abstract
The aim of the present research is to study the relationship between intellect capital components and performance evaluation indicators.For measuring intellectual capital, the study uses Pulic's method [Pulic, A. (2000).VAIC™-an accounting tool for IC management.International Journal of Technology Management, 20(5-8), 702-714.],which consists of three components of physical capital efficiency, human capital efficiency and structural capital efficiency.In the present study first, the value of the intellectual capital of the companies listed on Tehran Stock Exchange over the period 2006-2012 is calculated.Next, the relationship between the components of intellectual capital and financial return of the companies are evaluated.For calculating the financial performance 8 performance indicators in 5 groups presenting market value, profitability, activity, capital return, orientation on value creation are used.In the present research the statistical method used for data analysis is multiple regression and correlation coefficients.The selected sample of research includes 73 companies in continuous way for a time period of 7 years and the size of the company has been considered as a control variable.The findings indicate a positive and significant relationship between intellectual capital and financial performance of companies and a positive effect of the size of company on availability rate of intellectual capital and financial performance of a company.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| 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".