Financial well-being among Malaysian manufacturing employees
Bibliographic record
Abstract
Employees and financial well-being are two aspects that are closely related to each other, and have been deeply studied by researchers.Not only can financial well-being directly affect an individual, but it can also indirectly affect his/her organization as well as employer.Any level of financial employees' well-being, either low or high, will change their job performance.Thus, the purpose of this study is to determine the level of financial well-being among manufacturing employees in Batu Pahat, as well as to test the relationships between determinants and financial well-being among manufacturing employees in Batu Pahat.In this study, seven research hypotheses were developed to examine seven determinants, including age, income, gender, education, current job position, income, and marital status which influence employees' financial well-being.In this study, 220 employees at the production level were selected randomly from a manufacturing company in Batu Pahat, Johor, Malaysia.Then, a questionnaire was distributed to the employees.The data obtained were analyzed quantitatively using SPSS version 22.0.The results of this study revealed that the level of financial well-being was moderate and all of the determinants were positively related to financial well-being among the manufacturing employees.This quantitative study is important to the manufacturing industry in Malaysia in order to gain insight on the correlation between financial well-being and its determinants.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".