Perceived Challenges of Implementing An Integrated Talent Management Strategy at A Tertiary Institution in South Africa
Bibliographic record
Abstract
The aim of this study was to investigate and delineate the perceived challenges of implementing an integrated talent management strategy at a South African tertiary institution. The study was conducted at a relatively new university that opened its doors on the 1st of January 2015. Since the inception of the university under study, the institution has grown considerably but without proper policies and strategies in place to ensure its competitiveness and sustainability within the current Higher Education and Training sector in the country. A qualitative research methodology in the form of semi-structured interviews conducted with a convenience sample of 10 participants was employed to execute the study. The sample was drawn from the population of directors and official representatives of administrative, academic and support staff. The inclusion of these participants was premised on the idea that by virtue of their job description, they would be most exposed to talent management issues. Results of the study indicate that the major challenges experienced in implementing an integrated talent management strategy at the university include lack of management commitment and budget, as well as unionism and resistance to change amongst staff. As such, the primary recommendations of this study are for demonstrated commitment by university management towards accessing adequate finances to facilitate the implementation of a sound talent management strategy that will assist in promoting both the quality and longevity of the tertiary education institution in question.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".