Work experiences, satisfactions and psychological well‐being of female and male managers in the hospitality sector in Turkey
Bibliographic record
Abstract
Purpose The purpose of this paper is to explore gender differences in the work and career experiences of female and male managers and supervisors in the hospitality and tourism sector in Turkey. There is a belief that women tend to be disadvantaged in this sector as few women occupy the senior executive positions and those that do receive less pay. In addition, managerial and professional women in Turkey, in general, are less likely to achieve senior executive positions. Design/methodology/approach The paper reports research examining the work experiences, work and career satisfactions and psychological well‐being of females and males in managerial and supervisory positions in the hospitality and tourism sector in Turkey. Data were collected in 12 five‐star hotels operating in the Mediterranean and Agean region. Findings The paper finds, in answer to the research question – do female and male managers working in the Turkish hospitality and tourism sector report similar personal and work situation demographic characteristics, stable personality traits, work experiences, job satisfaction and levels of psychological well‐being – that the answer was Yes. Originality/value The paper is of value in pointing out that although its findings are positive, hospitality and tourism organizations still need to make efforts to support the career aspirations of their female managers and supervisors, and suggests elements which have proved useful in supporting women's career success and advancement.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".