Natural Environment Protection Strategies and Green Management Style: Literature Review
Bibliographic record
Abstract
The relationships between the Green Management Style (GMS) and Natural Environment Protection Strategies (NEPS) are rarely explored in scientific research. The nature of these relations is not fully explained in management sciences, and although these connections are important determinants for the choice between temporary and Sustainable Development (SD) in business organizations, they are accompanied by research gaps. The first research gap is recognized qualitatively in the literature review, which indicates the scarcity of theoretical research in the areas of NEPS and the GMS concerning Sustainable Development Goals (SGDs). The second quantitative research gap is dedicated to the rarity of empirical studies among business organizations engaged in NEPS and the GMS’s implementation. The third qualitative research gap lies in the difficulty of translating scientific assumptions from the theoretical background into business practice. This paper aims to present and explore the indicated research gaps and propose a theoretical model of the relationships between the GMS and NEPS. The adopted method used in this article is a Systematic Literature Review (SLR) supported by a bibliometric study performed in VOSviewer software. The results of the present study of relationships between the GMS and NEPS are explained by the Green Integrity Model (GIM). The green integrity between the researched elements can influence organizations’ decision-making processes related to development path directions, social and environmental responsibility, workers’ engagement, strategy communication, and organizational performance. In terms of the relationships between NEPS and the GMS, this can be seen as a part of the manner in which business organizations self-regulate.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.021 | 0.026 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".