Green Waves: How Leaders’ Actions Shape Employee Behaviours, a Moderated Mediation Model of Organizational Green Culture and Personality Traits
Bibliographic record
Abstract
In the recent decade, the literature on corporate greening, sustainability, and environmental performance has burgeoned. Organizational citizenship behaviour for the environment (OCBE), defined as employees’ voluntary behaviours that aim to contribute to the organization’s environmental management, has recently attracted considerable scholarly attention. Through a holistic review and synthesis of the current literature on OCBE, we aim to bridge the gaps in the literature and provide new and novel insights into the cascading effect of leaders’ OCBE throughout the organization. Specifically, we delineate the link between leaders’ OCBE engagement and followers’ propensity to engage in OCBE, a link mediated by organizational green culture (OGC). In addition, given the lack of attention to intra-personal variables in the OCBE literature, we also aim to explain the moderating effect of personality in the proposed mediated relationship. Theoretically, from the social learning and social exchange lenses, the paper contributes to the current literature by explaining the interplay between interpersonal (leaders’ behaviour) and organizational (OGC) variables in predicting employees’ OCBE. We also highlight the role of the personality trait of openness to experience as a boundary condition that can affect employees’ propensity to perform high-intensity OCBE. From a practical perspective, this paper offers valuable insights for management and HR practitioners who are committed to effective environmental management and sustainability.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".