Green Transformational Leadership and Environmental Performance: Insights from Bibliometric Analysis for Future Research Agenda
Bibliographic record
Abstract
In response to growing environmental challenges, green transformational leadership (GTL) has become a key driver of sustainable business practices and improved environmental performance.This study offers a comprehensive bibliometric analysis to map the intellectual landscape, research trends, and emerging themes in GTL and environmental performance.A total of 65 Scopus-indexed documents published between 2020 and April 2025 were analyzed using VOSviewer software to identify influential publications, prominent authors, key institutions, and leading countries shaping this evolving field.Six main thematic clusters emerge: strategic environmental management, employee green behavior, innovation, knowledge management, dynamic capabilities, and the link between GTL and environmental performance.The findings emphasize GTL's pivotal role in fostering a green organizational culture, driving innovation, and embedding sustainability within core business strategies.Furthermore, the study highlights the increasing recognition of environmental stewardship as both a regulatory obligation and a source of competitive advantage and long-term profitability.In addition, this research provides a roadmap for future studies and offers practical guidance for leaders and organizations aiming to integrate environmental sustainability into their strategies.By deepening our understanding of GTL's influence on environmental outcomes, this study contributes to both academic knowledge and the broader goal of global 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 | 0.021 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.078 | 0.184 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.015 | 0.011 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".