Do You Feel Safe Here? The Role of Psychological Safety in the Relationship between Transformational Leadership and Turnover Intention Amid COVID-19 Pandemic
Bibliographic record
Abstract
Despite their significant role in the performance of hotel industry, hotel workers are suffering from high rates of turnover, due to several reasons, particularly amid the COVID-19 pandemic. The pandemic has had numerous negative consequences on hotel workers, including their intention to leave the job or/and career. This study is an attempt to investigate the impact of transformational leadership on turnover intention amid COVID-19 and how psychological safety can intermediate this relationship. The study used a quantitative research approach via a pre-test instrument, self-distributed and collected from hotel workers at different regions in the Kingdom of Saudi Arabia (KSA). Valid responses from 1228 workers, analyzed through a structural equation modeling (SEM) of AMOS version 23, showed that transformational leadership has a significant negative impact on turnover intention as hypothesized. Nevertheless, it has a significant positive impact on psychological safety, whereas psychological safety has a significant negative impact on turnover intention. The most important finding of this study was the perfect mediating effect of psychological safety in the link between transformational leadership and workers’ turnover intention. This finding confirms that psychological safety has the ability to change the above-mentioned link. In other words, the presence of psychological safety ensures negative turnover intention, even if transformational leadership practices do not exist. The findings have implications for scholars and practitioners, especially in tourism and hotel context, in relation to the role of psychological safety and transformational leadership in creating a sustainable working environment to maintain a lower turnover intention.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".