Greening organizations through leaders' influence on employees' pro‐environmental behaviors
Bibliographic record
Abstract
Summary Climate change is a serious global issue that poses many risks to environmental and human systems. Although human activity is cited as the main cause of climate change and organizations significantly contribute to climate change, research that investigates workplace pro‐environmental behaviors remains scarce. We develop and test a model that links environmentally‐specific transformational leadership and leaders' workplace pro‐environmental behaviors to employees' pro‐environmental passion and behaviors. Structural equation modeling on data from 139 subordinate–leader dyads ( M ages = 37.42 and 40.17 years, respectively) showed that leaders' environmental descriptive norms predicted their environmentally‐specific transformational leadership and their workplace pro‐environmental behaviors, both of which predicted employees' harmonious environmental passion. In turn, employees' own harmonious environmental passion and their leaders' workplace pro‐environmental behaviors predicted their workplace pro‐environmental behaviors. These findings show that leaders' environmental descriptive norms and the leadership and pro‐environmental behaviors they enact play an important role in the greening of organizations. Conceptual and practical implications are discussed. Copyright © 2012 John Wiley & Sons, Ltd.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".