Analysing value added as an indicator of intellectual capital and its consequences on company performance
Bibliographic record
Abstract
Purpose The purpose of this paper is to analyse the role of value added (VA) as an indicator of intellectual capital (IC), and its impact on the firm's economic, financial and stock market performance. Design/methodology/approach The value added intellectual coefficient (VAIC™) method is used on 300 UK companies divided into three groups of industries: high‐tech, traditional and services. Data require to calculate VAIC™ method are obtained from the “Value Added Scoreboard” provided by the UK Department of Trade and Industry (DTI). Empirical analysis is conducted using correlation and linear multiple regression analysis. Findings The results show that companies' IC has a positive impact on economic and financial performance. However, the association between IC and stock market performance is only significant for high‐tech industries. The results also indicate that capital employed remains a major determinant of financial and stock market performance although it has a negative impact on economic performance. Practical implications The VAIC™ method could be an important tool for many decision makers to integrate IC in their decision process. Originality/value This is the first research which has used the data on VA recently calculated and published by the UK DTI in the “Value Added Scoreboard”. This paper constitutes therefore a kind of validation of the ministry data.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 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".