The Relationship between Changes in Corporate Governance Characteristics and Intellectual Capital
Bibliographic record
Abstract
The primary goal of this study was to investigate the effects of changes in corporate governance elements on a company’s valuable resources (such as intellectual capital and its components). Previous studies have examined the impacts of some corporate governance characteristics on intellectual capital performance as a whole and they have produced inconclusive and different results. This paper examines the effects of some corporate governance characteristics (i.e., the change in CEO, the evolution of auditor, the change in board independence, and the change in institutional ownership) on intellectual capital and its components (i.e., capital employed, human capital, and structural capital). This research is based on a quantitative study and the selected sample contains 1170 observations from 220 companies listed on the Middle East Stock Exchange from 2011 to 2018. The research findings show a positive and significant relationship between an increase in institutional ownership and intellectual capital and its two components (human capital and structural capital). The results support the relationship between a change in auditor and intellectual capital and human capital efficiency. Further, a positive and significant association was found between an increase in board independence and human capital. However, no relationship was found between a change of CEO and intellectual capital or any of its components. This study extends the research field of corporate governance by studying the effects of changes in corporate governance characteristics on intellectual capital for the first time. Given the significant role of intellectual capital in the performance of firms, this study provides essential information to organisations’ decision makers.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".