Impact of Aggregated Cost of Human Resources on Profitability: An Empirical Study
Bibliographic record
Abstract
In Nigeria, the past decades have witnessed a transition from manufacturing to service based economics. The fundamental difference between the two sectors lies in the very nature of their assets. The former sectors are driven by physical asset like plant, and machinery while the later sector is driven by knowledge, skill and attitude of the employee. This had lead to a paradigm shift in expenditure on those assets and interest. As expenditure on human resource increases also lead to the demand for its inclusion in financial report. Expenditure on human resource has two components, the expense and the investment. Conventional accounting treat both as revenue expenditure, this aggregated approach has a negative effect on the profitability. This study examined the relationship between (1) The aggregated cost of human resource and organizational profitability. (2) The effect of the disaggregated cost of human resources on organization profitability. Data was extracted from internal source using a structured information card and annual financial report. Regression analysis was used. The findings show that there is a positive relationship between profitability and human resource cost. It also shows that changes in profitability can be explained when the expenditure on human resource are segregated into revenue expenditure and capital expenditure. The study recommends amongst other, that BETA NIG PLC should imbibe the culture of capitalizing and reporting all investment on human resource that improve the quality and productivity.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".