Investigating the Cascading Effect of Leaders’ OCBE: A Moderated Mediation Model
Bibliographic record
Abstract
Scholars and practitioners have expounded on the importance and urgency of corporate environmental sustainability. As such, today’s organizations are more concerned about their environmental performance and exploring ways to encourage and facilitate employees’ pro-environmental behaviours. In recent years, research attention has been directed to the management of employee pro-environmental behaviours in the workplace. Organizational citizenship behaviour for the environment (OCBE) is employees’ voluntary behaviours that can help facilitate the effective environmental management of the organization. Drawing upon social learning theory (SLT) and social exchange theory (SET), this conceptual paper aims to delineate the cascading effect from leaders’ OCBE to followers’ OCBE and explore its underlying process while explaining the impact of organizational culture on employee behaviour. Furthermore, it discusses how and why a personality trait (i.e. openness) can increase employees’ tendencies to engage in high-intensity OCBE (i.e. OCBE with short-term costs and long-term benefits). By proposing a moderated mediation model that depicts a cascading effect, this paper will be of value to both academics and management practitioners. It will assist organizations in hiring and developing employee competences in environmental sustainability. Leadership is also emphasized relating to hiring and promoting those with expertise in and passion for environmental 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 | 0.011 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 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".