The impact of fintech-based eco-friendly incentives in improving sustainable environmental performance: A mediating-moderating model
Bibliographic record
Abstract
China is the largest emitter of greenhouse gases globally, responsible for a substantial portion of the world's total carbon dioxide emissions. Many researchers investigated this issue; however, the literature is silent on how FinTech-based incentives can improve environmental performance. The research aims to shed light on the complex relationships among FinTech incentives, green consumer behavior, environmental consciousness, and environmental performance. Data was collected from 380 respondents representing diverse roles in the manufacturing industry. We used Smart-PLS and SPSS to test our hypothesis. The results confirm a positive relationship between FinTech incentives and green consumer behavior. However, consumer demographics and environmental awareness do not significantly moderate this relationship. The mediation analysis reveals that green consumer behavior mediates the relationship between FinTech incentives and environmental performance, while environmental consciousness mediates the relationship between FinTech incentives and green consumer behavior. Additionally, green consumer behavior mediates the relationship between environmental consciousness and environmental performance. The study's findings suggest that FinTech incentives effectively encourage eco-friendly choices, positively influencing environmental performance. This research contributes valuable insights for policymakers and businesses seeking to design effective environmental strategies and promote sustainability in the manufacturing industry. By leveraging FinTech incentives to encourage eco-friendly choices and foster environmental consciousness, businesses can contribute to a more sustainable future, aligning with global efforts to address environmental challenges and foster responsible consumption patterns in China and beyond.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 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".