The Influence of Financial Literacy, Saving Behaviour, and Financial Management on Retirement Confidence among Women Working in the Malaysian Public Sector
Bibliographic record
Abstract
Awareness of retirement confidence has been found to be low in many people, especially in women. Much of past research has revealed that women consistently perform a poor level of retirement confidence compared to men. This study aims to examine the influence of financial literacy, saving behaviour, and financial management on retirement confidence among women working in the Malaysian public sector. Multi-stage random sampling technique was applied as the sampling technique in this study. 708 respondents participated in this study. This study applied Pearson Correlational analysis to determine the relationship between the variables. The findings reveal that retirement confidence is positively correlated with financial literacy, saving behaviour, and financial management. Furthermore, multiple regression analysis was applied to determine the predictors of retirement confidence. This study concludes that financial literacy, saving behaviour, financial management, and financial status are significant predictors of retirement confidence among working women, with financial management as the major factor contributing towards retirement confidence. The findings of this study have practical implications for financial advisors in helping working women to be more aware of their future retirement life financial needs and to prevent financial crisis in later years.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".