Internal Versus External Staffing in Nigeria: Cost-Benefit Implications
Bibliographic record
Abstract
The internal recruitment is perceived in some quarters as the most favourite source of staffing an organization in stable and developed companies. The system needs a strong support from other HR processes in order to enhance morale and promote productivity otherwise it could yield disappointed and unproductive employees in the organization. Such HR processes include effective succession planning and consistent performance management to ensure success of the internal staffing. Though internal staffing may not bring new skills, competencies and fresh blood into the organization but it could build a strong loyalty as the employees have a chance to change their positions after a period of time and are not pressed to look for opportunities in the external labour market. The external recruitment on the other hand, brings new people to the organization. It allows the organization to define the right requirements especially where the labour market is full of potential candidates and to select candidates that best suit the organization’s demands. There is no gain saying the fact that external staffing system with effective campaigning or advertisement could increase the popularity and enhance the image of the organization in the labour market, however, the system is expensive and takes a lot of energy from the HR functionaries in the course of handling job candidates in the selection process. In the light of the pros and cons of the two system of recruitment, this paper seeks to determine the cost benefit implications of internal vis-à-vis external system by considering the issues involved from four perspectives: economic, socio-psychological, financial and political context. It concludes that the usage of either internal or external staffing system to fill vacant positions should be determined by availability of qualified candidates, size of the organization and the desire to maintain and promote organizational culture.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".