The Impact of Intellectual Capital on Firms’ Performance: Evidence from Saudi Arabia
Bibliographic record
Abstract
The purpose of this study is to empirically investigate the relationship between intellectual capital (IC) measured by the value-added intellectual coefficient (VAIC) and firms’ performance (FP) in the Saudi context. Data are drawn from a sample of 25 Saudi firms listed on the Saudi Stock Exchange (Tadawul) for the period 2015-2018. Using the VAIC model, the multiple linear regression models were constructed to examine the relationship between intellectual capital (IC) and firms’ performance (measured in terms of financial and market performance). The findings indicate that there is a positive association between overall intellectual capital efficiency as well as each of its three components (human capital efficiency, structural capital efficiency, capital employed efficiency) and the firms’ financial performance. Additionally, there is a positive association between human capital efficiency(HCE), structural capital efficiency (SCE), and the firms’ market performance. Overall, the findings suggest that human capital efficiency (HCE) has a significant and positive impact on firms’ financial and market performance in Saudi Arabia. The VAIC method may be a useful tool for managers and investors in their decision process. This is the first study about the impact of intellectual capital on firms’ performance in four industry groups in Saudi Arabia using the VAIC model.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".