Talent Management Implementation at an Open Distance E-Learning Higher Educational Institution: The Views of Senior Line Managers
Bibliographic record
Abstract
The war for talent remains a challenge that many organisations face but more so for distance education institutions to deliver on its mandate to provide effective online academic offerings. The question that remains is: How can intellectual capital be managed effectively in order to recruit and retain talent that is necessary for success? This study was conducted at a mega open and distance learning institution, and this institution has identified talent management as one of the key strategic initiatives to ensure institutional strategic goal attainment and adopted an inclusive/developable talent approach as its framework. The aim of this article is to report on the perceptions of senior line managers regarding their experience with implementing the talent management strategy in their operational areas at the institution. This study adopted a qualitative approach and purposive sampling was used to select interviewees. The population group included chairpersons of 26 talent committees who are senior line managers and 11 of them were interviewed. Participants were of the opinion that policies and strategies do not always support the implementation of talent management in their respective environments. The findings show that although the university embraces the inclusive/developable talent approach in its strategy, the impact thereof is inhibited by a lack of methodological implementation, a lack of integration of supporting Human Resources policies with talent management, and insular line manager discernment.
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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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".