“We are like a family!”: Flexibility and Intention to Stay in Boutique Hotels in Turkey
Bibliographic record
Abstract
This study focuses on a unique type of small business—boutiquehotels in Istanbul, Turkey—, and aims to understand whetheremployers’ use of internal flexibility strategies is associatedwith boutique hotel employees’ intention to stay in theirorganization. Internal flexibility strategies refer to shiftwork, longworkweeks, unpaid overtime, and working preferred hours. Our study focuses on the experience of employees in boutique hotels in Turkey, which is one of the largest economies globally with its hospitality sector being the eighth largest in the world (Zeytinoglu et al., 2012a and 2012b). We test the conceptual model of internal flexibility strategies and intention to stay using data from 20 interviews and 122 surveys with employees in 32 boutique hotels. As our qualitative and quantitative study shows, shiftwork decreasesboutique hotel employees’ intention to stay, but long workweeksand working unpaid overtime do not affect the intention to stay.Furthermore, as our qualitative study shows, the close family-like workenvironments that exist in boutique hotels contribute to theemployees’ intention to stay. As our respondents said in thequalitative part of the study: “‘We’re like afamily!’ and cannot leave our ‘home’!”, despitenot liking the shiftwork. By examining the relationships between flexibility and intention tostay in small workplaces such as boutique hotels, our study contributesto both the academic literature on internal labour flexibility and tothe model of intention to stay. For practitioners, this study providesevidence on the use of the type of internal labour flexibilitystrategies used in boutique hotels, contributing to the understandingof how boutique hotels can be successful in retaining valuable staff.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".