Human Capital Accounting Implications on Firm Market Value: A Survey of Kenyan Private Universities
Bibliographic record
Abstract
Purpose-The study examined the findings of an empirical evaluation of Human Capital (HC) accounting implications on firm market value among Kenyan private universities. Design/methodology/approach- a Cross-sectional survey on finance and human resource directors in Kenyan private universities.Findings- Kenyan Chartered Private Universities were successful because they accounted for, and reported their HC as material investments. However, further research was recommended to establish whether: the 4.2% who did not enjoy a good reputation and image consecutively for the past three financial periods; the 47% who did not enjoy easy access to Kenya’s capital markets consecutively for the past three financial periods; the 25% who did not retain their quality HC consecutively for the past three financial periods; as well as the 30% who did not enjoy high ROI consecutively for the past three financial periods: did so solely because they did not account and report their HC as material investments or there were indeed other factors motivating the results. Originality/Value-Accounting for HC is a big deal in Kenyan Knowledge-Information-Service-Sector (KISS) firms such as private universities because HC is the intervening factor for competitive advantage: Yet the discipline is unexplored in existing Kenyan empirical works.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".