Labor Shortages in the Hospitality Industry: The Effects of Work-life Balance, Employee Compensation, Government-Issued Unemployment Benefits and Job Insecurity on Employees' Turnover Intentions
Bibliographic record
Abstract
The purpose of this quantitative research was to identify the factors causing labor shortages in the hospitality industry in the post-pandemic era. Specifically, it examined the effects of work-life balance, employee compensation, government-issued unemployment benefits, and job insecurity on employees' turnover intentions. The research methodology employed in this study was a quantitative survey, with a sample size of 385 participants from the hotel, restaurant, bar industry, and food service sector. The findings indicated work-life balance, employee compensation, and job insecurity had a significant impact on employees' turnover intentions, as the null hypotheses for these factors were rejected. However, the government-issued unemployment benefits (EDD) did not show any significant impact, indicating further research is needed to gain deeper insights into the potential influence of these benefits. These findings contribute to the understanding of the challenges faced by the hospitality industry in retaining employees and highlight the importance of addressing work-life balance, compensation, and job insecurity to mitigate employee turnover.
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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 | 0.003 | 0.005 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".