Fostering Business Success Through Green Practices: The Role of Green Entrepreneurship and Innovation in Enhancing Firm Performance
Bibliographic record
Abstract
The tourist industry is one of the business actors that must be more astute and astute in their business field selection in response to changes in the business environment, particularly those brought about by external causes.Businesses must change their operations to become more ecologically friendly due to stakeholders' increased awareness of environmental issues, both internal and external.This research aims to examine and analyze how green entrepreneurship affects business performance and green innovation.Additionally, the moderating effect of the business environment and the mediating role of green innovation within the connections are also considered in this study.245 business players and strategic decision-makers in green tourism enterprises based in four East Javan cities-Malang, Surabaya, Pasuruan, and Bojonegoro-responded to this study.Respondents are given online surveys to complete to gather data, further examined using the Smart PLS software and structural equation modeling with partial least squares.The conclusions show that green innovation drives company performance and that green entrepreneurship substantially impacts both.Additionally, this research validates that green innovation partially mediates the relationship between green entrepreneurship and company performance.It has been established that the business environment cannot enhance the impact of green entrepreneurship on firm performance, hence serving as a moderating factor.Ultimately, our research suggests that green entrepreneurship has a significant role in the eco-friendly tourism context since it can improve business performance and spur innovation.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".