Lecturer’s Perspective on Talent Management in Private Higher Learning Institutions in Kuala Lumpur, Malaysia
Bibliographic record
Abstract
Talent management has been an ongoing focus in teaching and career development among lecturers in universities. However, this effective practice lies in the conduct of certain factors in an organisation. Some of the important factors that contribute to talent management of an organisation are; the ability of lecturers to perform, organizational culture and retention practice of an organisation. Hence, this paper took a milestone in explaining the relationship among talent management and three antecedent factors; performance, organisational culture and retention. Importantly, the research focuses on academicians who are teaching Information Technology related subjects. The leading universities in Malaysia have a tendency to lose competent academicians thus creating a gap in the organisational outcome. Thus, respondents were sampled from Private Higher Learning Institutions in Kuala Lumpur, Malaysia. The data was collected from 133 respondents who have been teaching in IT related modules. Hypotheses were built based on the relationship between variables and analysed using Pearson Correlation in via the SPSS software. The results showthat two hypotheses are not supported except for one of the hypothesis on retention has indicated a significant relationship with the talent management practice of the university. Information Technology is a fast growing industry as lecturers in this field need to be constantly updated in their knowledge, skills and ability. This requires talent management. Academicians who are unable to do this with the support and motivation of an organisation may not be able to offer their services in the university. Consequently, this can lead to poor outcome on knowledge delivery to students or the turnover rate may be affected. Overall, this paper has called for good human resource practices for lecturers in the teaching profession.
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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 | 0.001 |
| 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 | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".