How do increased job demands resulting from rationalization of costs exhaust flight attendants and push them to leave? An international study
Bibliographic record
Abstract
Cost effectiveness becomes a priority of airlines pushed organizations to achieve their objectives of being cost leader. This strategy generated multiple employees' related issues such as emotional exhaustion, work-family conflict, and a high turnover. However, these issues still need more research in the airline sector. In the current study, we aim to explore the indirect effect of increased job demands on intention to leave via work-family conflict, emotional exhaustion that provides an insight of the route towards flight attendants’ intention to leave. Data was collected from 1664 flight attendants working in three countries (Canada, France, and Germany) using questionnaires online. Structural equation modelling (SEM) was used to evaluate the said relationships. Results exhibit that all the factors contributing towards intention to leave are significantly and positively interrelated. The results also revealed that work-family conflict positively mediates the relationship between increased job demands and emotional exhaustion and this last mediates the relationship between increased job demands and intention to leave. Furthermore, psychosocial safety climate acts as a moderator on the relationships between increased job demands and work-family conflict and on the link between this last variable and emotional exhaustion.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".