Turnover Intention, Organizational Commitment, and Specific Job Satisfaction among Production Employees in Thailand
Bibliographic record
Abstract
The purpose of this study was to examine how turnover intention relates to the attitudinal variables of organizational commitment and job satisfaction. We highlighted three specific facets of job satisfaction—personal development, human resources policy, and supervision—in a research context of Thailand, an important emerging market. A Thai company that operated a business in the fishing industry participated in this study, and the sample consisted of 255 employees who had worked for the company at least 3 months. The results of our analysis using a structural equation model indicated that Thai employees’ satisfaction with supervisors significantly affected turnover intention, while personal development and human resources policy indirectly influenced turnover intention through organizational commitment, which strongly mediated the relationship. Based on these findings, we concluded that the specific job satisfaction facet of supervision tends to be a direct determinant of turnover intention, while the two facets of personal development and human resources policy are likely to be an indirect determinant mediated by organizational commitment.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".