Human Capital Reporting (HCR) and Shareholder Value Maximization in Listed Manufacturing Firms in Nigeria
Bibliographic record
Abstract
This study examines the impact of human resource reporting (HCR) or disclosures on share price and earnings potential measured by the earnings per share. It adopts an ex-post causal research design and employs secondary data retrieved from annual reports of 30 selected manufacturing firms in Nigeria. Data was analyzed using descriptive statistics, correlation analysis and the quantile regression techniques. The research outcome from the distributional dynamics for share price tends to highlight that the effect of HRD-Index is significant at 5% for firms at high levels above average financial performance at Q[0.2.] - Q[0.4] and also significant at 5% for firms at average levels of firm value Q[0.5] and even below average levels Q[0.6]-Q[0.9]. Finding thus highlights that the impact of human resource disclosures on share price or market value may not necessarily be a function of the share price levels. The distributional dynamics for EPS used as the measure for earnings potential is similar to that which was observed for Share price and tends to highlight that the effect of human resource disclosure is significant at 5% for firms at high levels above average earnings per share measure of financial performance at Q[0.1], Q[0.2.], Q[0.3.] and Q[0.4.] and also significant at 5% for firms at average levels of financial performance Q[0.5] and even below average levels Q[0.6]-Q[0.8]. The recommendation is that human resource investments should not been looked at as an expense but as a competitive strategy of the firm.
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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 | 0.000 | 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) | 0.001 | 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 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".