Impact of intellectual capital on corporate performance: evidence from the Arab region
Bibliographic record
Abstract
Purpose The purpose of this paper is twofold: first, to fill a gap in the intellectual capital (IC) literature by providing insights into the relationship between IC and corporate performance among Arab companies and second, to challenge the validity of the Value Added Intellectual Coefficient (VAIC) as a measure of IC’s contribution to performance. Design/methodology/approach The research sample included 100 publicly traded Arab companies selected by Forbes Middle East and ranked as top performers in terms of sales, profits, assets, and market value. The methodology included assessing the impact of IC components on company earnings, profitability, efficiency, and market performance for the period between 2011 and 2015. Research hypotheses were tested through the presentation of descriptive statistics, normality tests, correlation matrix, and multiple regression models. Findings The research yielded ambiguous results. Earnings and profitability were significantly affected by structural and physical capital; efficiency was determined primarily by physical capital; and market performance was mainly influenced by human capital. Research limitations/implications The main limitation of the research comes from disadvantages of VAIC as the measure of IC’s contributions to performance. Originality/value The paper fills a void in the study of IC and corporate performance among Arab companies.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| 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".