The influence of workplace inequities through the employment lifecycle on commitment and turnover in the hospitality industry
Bibliographic record
Abstract
The COVID-19 pandemic exacerbated existing labor challenges in hospitality and tourism, specifically around gaps in gender and leadership equity where females comprise a disproportionately high percentage of the hospitality workforce yet represent a disproportionately low proportion of senior management. This research explores the employee experience, unpacking attitudes and perceptions of their journey through the employment lifecycle (entering, working, and leaving an organization). Certain differences appeared by gender around perceived diversity management, wage satisfaction, and feelings of justice around job decisions and justified compensation. Female employees identified far more barriers existing for equity deserving groups than their male counterpart. Regression analyses showed that ethical and fair hiring practices play a significant role in improving organizational commitment and turnover intentions, and when working in an organization, both job and career satisfaction have similar impacts to commitment and turnover. Contextual factors, such as being married and having children, increase commitment while decreasing intentions to leave. However, those working only part-time hours, particularly frontline workers, demonstrated significantly decreased levels of commitment and increased intentions to leave. Effective management of equity and diversity showed significant benefits to increasing a committed workforce. Feelings of being burnt out appeared to have significant influence on a desire to leave.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".