Explaining Generation-Y Employees’ Turnover in Malaysian Context
Bibliographic record
Abstract
Among the various speculation published in media reports about the reasons why generation-Y workforce in Malaysia changes job frequently include dissatisfaction with pay and fringe benefits, seeking work-life balance, perceived status work-values fit, normative commitment, perceived availability of alternative job and job hopping, This study aims to empirically justify or refute some of the anecdotal information about generation-Y employees’ decision to leave an organisation in the context of Malaysia. Using structural equation modelling with a sample size of 150 respondents, this study revealed that satisfaction with payment and fringe, perceived availability of alternative job and job hopping are significant to generation-Y employees’ intention to quit. Additionally, normative commitment as part of employees’ loyalty is insignificant to generation-Y employees’ intention to quit. This study provides implication to human resource (HR) managers that generation-Y employees’ intention to quit may not be entirely due to HR strategies. Instead, cultural and economic factors play an important role in such decisions. However, there are other reasons that are widely held about generation-Y employees’ intention to quit, which may not be held true or empirically validated. Lastly, normative commitment does not influence their intention to stay or to leave an organisation, as their loyalty is to their personal lives.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".