Impact of Green Transformational Leadership and Green Transactional Leadership on Green Organizational Citizenship Behaviour by Mediating Role of Green Self-Efficacy
Bibliographic record
Abstract
The aim of this research paper is to apply the results and findings of the examination carried out by studying the impact of green transformational leadership and green transactional leadership on green organizational citizenship behaviour by the mediating role of green self-efficacy in the food and beverage sector of Pakistan. Research data was gathered by using a survey method consisting of a structured questionnaire which was given to the respondents comprising of managers and employees of various organizations. The research data gathered from convenience sampling was examined by making use of the software of smart PLS. The findings of the study concluded that both green styles of leadership, i.e. green transformational leadership and green transactional leadership positively impacted green organizational citizenship behaviour by the mediating role of green self-efficacy. The research also concluded that green transformational leadership has a more positive impact on green organizational citizenship behaviour as compared to green transactional leadership. In summary, these findings can assist managers and leaders in developing and implementing strategies which are effective to improve the awareness, behaviour and attitude of the employees in contributing towards environmental sustainability in the workplace.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".