Green Transformational Leadership’s Impact on Employee Retention: Does Job Satisfaction and Green Support Bridge the Gap?
Bibliographic record
Abstract
This study explores the impact of green transformational leadership (GTL) on employee retention in the hospitality sector, with a specific focus on the mediating roles of employee satisfaction and perceived green organizational support (PGOS). Grounded in self-determination theory and organizational support theory, the research examines both the direct and indirect effects of GTL on employee retention. GTL fosters a sustainability-driven work environment, motivating employees through shared environmental values, which enhances their job satisfaction and perceived organizational support. These factors, in turn, strengthen employees’ commitment and willingness to stay within the organization. Using SmartPLS 3 for structural equation modeling (SEM), data collected from hospitality employees were analyzed to assess the hypothesized relationships. The findings confirm that GTL positively influences employee retention, both directly and indirectly, through the mediating effects of employee satisfaction and PGOS. This study contributes to the growing body of knowledge on sustainable leadership by highlighting the crucial role of green leadership practices in improving employee retention in environmentally conscious organizations. Implications for managers and policymakers in the hospitality sector are discussed, emphasizing the need for sustainability-driven leadership approaches to enhance workforce stability.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".