Intellectual capital as a longitudinal predictor of company performance in a developing economy
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract This study assesses whether intellectual capital (IC), measured using the Value‐Added Intellectual Coefficient (VAIC), can predict the financial and market performance of listed companies in a developing economy. Panel data from all 174 companies listed on the Kuwait Stock Exchange were analyzed. Four company performance measures were investigated: return on assets, return on equity, market/book value, and market capitalization. Eight competitive longitudinal models were evaluated using SEM–PLS, as well as the 1‐year, 2‐year, and 3‐year lags. VAIC possesses significant predictive power on company performance, but only on return on assets and return on equity, with a stronger predictive power for the 2‐year lag. When analyzing the 3‐year lag, the model fit decreases significantly. This suggests that VAIC has no significant predictive power on analyzed market performance measures. Most extant literature on IC does not explicitly quantify its lagged effect and predictive power on company performance. Additionally, existing research focuses less on developing economies. The research was conducted in a developing economy with a relatively young and inefficient financial market. This rationalizes the findings in which IC cannot predict market performance. Additionally, the time span considered is only 5 years from the 21 years analyzed. Useful managerial insights on the evident lagged effect and predictive power of IC in a developing economy are provided. Quantifying the effect size adds value to the further understanding of IC's nature.
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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 | 0.001 |
| 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) | 0.001 | 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 it