Fostering sustainable behavior through green leadership: The mediating role of environmental consciousness and moderating effect of goal clarity
Bibliographic record
Abstract
This study investigates the mechanisms through which green transformational leadership (GTL) and green authentic leadership (GAL) influence employees’ green behavior for sustainable development (EGB). Drawing from social cognitive and goal-setting theories, we examined the mediating role of environmental consciousness (EC) and the moderating role of goal clarity (GC) using survey data collected from 532 employees across diverse organizational contexts in China. Partial least squares structural equation modeling (PLS-SEM) via SmartPLS was employed to analyze the data. Results indicate that GTL and GAL positively impact EC and EGB directly. EC significantly mediates the relationship between both leadership styles and EGB, highlighting its critical psychological function in translating leadership practices into concrete environmental actions. Additionally, GC significantly strengthens the positive relationship between EC and EGB, demonstrating the importance of clearly articulated sustainability objectives in fostering sustainable workplace behaviors. These findings provide essential theoretical insights and practical implications for enhancing sustainability performance through targeted leadership practices, environmental awareness initiatives, and effective sustainability goal-setting strategies.
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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 | 0.007 |
| 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 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".