Bibliographic record
Abstract
This study investigates the job burnout components impact on manufacturing workers job commitment since burnout known as a syndrome that influences employees' motivation and their commitment level.As well, each organization also aims to increase the company profit and productivity by reducing the level of strain among their workers which may reduce the performance and commitment level.The present study also aim to investigate if there is any significant relationship between emotional exhaustion, personal accomplishment and depersonalization towards job commitment among the manufacturing workers in Melaka.There have been limited study done in manufacturing sector and this study focused on manufacturing sector, in order to minimize the negative effect on employee productivity and their commitment level.The Maslach Burnout Instrument-Human Service Survey (MBI-HSS) and Employee Commitment Survey (ECS) were used in this study.This study examines the direct relationship between emotional exhaustion, depersonalization and personal accomplishment on job commitment.A quantitative approach with descriptive analysis method and multi-stage sampling method were used in this study.Where, cluster method was conducted followed by stratified and simple random sampling method.A total of 780 questionnaires were distributed to the manufacturing workers in Melaka who had agreed to participate in this study.However, out of it only 518 questionnaires were returned and about 383 questionnaires were usable for further analysis.Results showed that emotional exhaustion and depersonalization were significantly negatively associates with job commitment while personal accomplishment was significantly positively associates with job commitment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.942 | 0.933 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".