The Relationships among Emotional Demand, Job Demand, Emotional Exhaustion and Turnover Intention
Bibliographic record
Abstract
The objectives of this paper are to assess the levels of job demand, emotional demand, emotional exhaustion and employee turnover intention and to examine the relationships among these concepts in the context of three selected apparel manufacturing firms in Eastern region of Sri Lanka. Employee absenteeism and turnover are key issues of apparel firms in Sri Lanka. In order to achieve the objectives of this paper, a questionnaire based survey was conducted among 153 employees of apparel firms and collected data were analyzed by using univariate and bivariate techniques. The findings of this paper revealed that there is a strong positive relationship between emotional demand and emotional exhaustion, emotional demand and turnover intention, job demand and turnover intention, and emotional exhaustion and turnover intention. At the same time, there is a moderate positive relationship between job demand and emotional exhaustion. The findings of the study have various managerial implications for the apparel manufacturing firms to prevent or control employee stress, absenteeism and turnover related issues and to develop good labour-management relationship.
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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 | 0.003 |
| 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 | 0.001 |
| 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".