Driving Sustainable Change: Green Transformational Leadership, Job Crafting, and Work Engagement in Frugal Eco-Innovation
Bibliographic record
Abstract
Frugal Eco-Innovation (FEI) has become crucial for supporting the hospitality industry in third-world countries facing sustainability challenges.FEI can potentially enhance the environmental performance of employees in the hospitality sector.However, there is a lack of empirical studies on this topic in the existing literature.Therefore, our research aims to investigate the impact of Green Transformational Leadership (GTL), Green Work Engagement (GWE), and Green Job Crafting (GJC) on frugal eco-innovation.We conducted our study using a self-administered questionnaire, collecting data from 298 employees working in tourist hotels in Bali.We employed covariance-based structural equation modeling to test our hypotheses.Our statistical analysis revealed a significant positive relationship between green transformational leadership and overall frugal eco-innovation (b=0.215,t=2.954, p<0.003).This result suggests that GTL significantly promotes frugal eco-innovation for sustainable practices among green hoteliers.Furthermore, our results indicated that GTL is equally important for both green job crafting and green work engagement.GTL practices were positively correlated with both variables (b=0.377,t=5.289, p<0.000; b=0.542, t=7.744, p<0.000).Lastly, we found that GTL has a significant indirect effect on FEI, mediated by GWE and GJC, with a partial mediation effect.This empirical investigation is the first that provides an organized way to examine the effects of green transformational leadership on frugal eco-innovation and recommends the most effective means for hoteliers to embrace the best policies for sustainable practice.This study has important policy implications for harnessing individual development into resource-constraint green management practice.Additionally, GTL provides a supportive environment for employees with green crafting and engagement to realize their green potential.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| 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".