Role of HRM in Talent Retention With Evidence
Bibliographic record
Abstract
Talent management and retention are increasingly seen as an essential practice in business sustainability strategies. It has since expanded from the sporting fraternity and the arts, particularly in the entertainment industry to become a global practice. This paper discusses the purpose and strategies used for talent management. Using a case study approach that combines the use of Reflexive Account (a retrospective analysis) and content analysis of firm reports, this paper identifies the Human Resource management practices implemented by one firm, MTN-Uganda as a case that provides human resource practitioners with evidence of the practical utility of various talent management and retention strategies. The primary sources of information used in reporting on the case were obtained through reflexive analysis (2012-2015) and content analysis of firm reports (2007-2018). Information gaps were filled in by contact and answered queries through the Department of Corporate Services at MTN Uganda.Although there are many strategies for implementing talent management programs, their success is primarily pegged on the use of a mixed approach, with the Human Resource functions being supported by other management divisions to realise the return on investment sought through the implementation of talent management programs.
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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 | 0.134 | 0.386 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".