Reframing talent acquisition, retention practices for organisational commitment in Malaysian SMEs: A managerial perspective
Bibliographic record
Abstract
This paper explores whether employee commitment to the organisation is influenced by talent acquisition and retention practices. How do SMEs retain talent and encourage commitment and job embedding when their very existence is in doubt? The talk these days is about layoffs, terminations, reduced pay, no-pay leave and redeployment; how will SMEs fare in this scenario? This qualitative study includes in-depth interviews conducted using a social constructivism approach in order to gain a better understanding of the world in which we live and work. Individual meanings are formed not entirely within the individual, but through interaction with others, the social, where meaning is given. The template analysis method is used to analyse the interviews, which include specific themes. The findings point to a misalignment between talent acquisition, talent retention practices and organisational commitment. There has been no clear development in terms of how both can complement each other. As a result, the dimensions of person-organisation and person-job fit serve as a link between talent acquisition, retention and commitment. This contributes to higher levels of employee commitment, job embeddedness and an internal culture that may influence employee retention decisions. At the interpersonal level, SME owners must understand, nurture and motivate their talent. As an effective employee retention tool, monetary incentives play only a minor role. Talent retention is based on implementing a structured talent acquisition practice that includes a highly targeted cost-effective hiring plan. Talent acquisition includes fit dimensions that demonstrate a nuanced perception of individualised consideration, such as flexible work, belonging, career growth and interpersonal relationships.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".