The Impact of Intellectual Capital on Firm Value: Empirical Evidence From Vietnam
Why this work is in the frame
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Bibliographic record
Abstract
The paper aims to investigate the impact of intellectual capital on firm value in the context of Vietnam. The research sample includes 61 manufacturing companies listed on Vietnam stock market for the period from 2013 to 2018. Three statistical methods approaches are employed to address econometric issues and to improve the accuracy of the regression coefficients include Ordinary Least Square (OLS), Random Effects Model (REM) and Fixed Effects Model (FEM). This research uses value-added intellectual capital (VAIC) to measure the intellectual capital of a firm. Value-added intellectual capital (VAIC) is considered as an effective measure by which a company uses material, financial, and intellectual capital to increase. The VAIC includes the sum of three components: Human Capital Efficiency (HCE), Structure Capital Efficiency (SCE) and Capital Employed Efficiency (CEE, including physical and financial capital). In this paper, firm value is measured by Tobin’s Q ratio. Some control variables such as leverage, firm size, growth rate, and state capital are used in the regression model that pointed out the impact of intellectual capital on a firm value. The empirical results show a statistically significant positive impact of value-added intellectual capital (VAIC) on a firm’s profitability. This evidence provides a new insight to managers on how to improve the value of manufacturing companies listed on Vietnam stock market.
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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 | 0.001 | 0.014 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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