Effective environmental strategy or illusory tactics? Corporate greenwashing and innovation willingness
Bibliographic record
Abstract
Abstract Corporate greenwashing is an unethical environmental strategy that has received extensive attention from practitioners and academics; however, little is known about its influence on innovation willingness. The purpose of this study is to fill this important gap by exploring the influence mechanism of corporate greenwashing on innovation willingness through underperformance duration. Drawing on resource‐based theory, Homo economicus , and performance feedback theory, this study uses data of 610 Chinese A‐share listed heavily polluting companies (3819 company‐year observations) from 2013 to 2019. The results show that there is a significant inverted U‐shaped relationship between corporate greenwashing and innovation willingness, with underperformance duration partially mediating this relationship. The findings advance our understanding of the non‐economic consequences of corporate greenwashing, enrich the literature on corporate greenwashing and innovation willingness, and offer valuable insights for interdisciplinary studies between environmental strategy and corporate innovation. Practical implications provide policymakers with recommendations on how to prevent corporate greenwashing in environmental responsibility.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".