Women in hospitality and tourism: a study of the top-down and bottom-up dynamics
Bibliographic record
Abstract
Purpose This study aims to investigate the impact of women’s representation at one hierarchical level on women’s representation above or below that level. No past research investigated these effects in the hospitality and tourism industries. The mixed results of research in other industries and across industries demand tests of curvilinearity and moderators. Design/methodology/approach Using annual equality reports, a panel data set for 2010–2019 was created for the hospitality and tourism industries. The sample of 581 organizations had up to 5,810 observations over the 10 years. Findings The analyses show the following effects of women’s representation: an inverted U-shape from management to non-management, a U-shape from non-management to management and a U-shape from management to the executive team, with more pronounced effect in small organizations. Practical implications To increase the number of female employees, organizations should invest their resources in hiring and retaining female managers until a gender balance is reached while managing any backlash from men. The results suggest that organizations with more than 40% of women non-management employees and 50% of women managers start `experiencing positive bottom-up dynamics. Thus, efforts need to be made to attract and retain a women’s pipeline at the non-management and management levels. Originality/value This study delivers pioneering evidence of the top-down and bottom-up phenomena in hospitality and tourism. It refines evidence of such effects found in past research conducted in other industries and across industries.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".