Green Technology Innovations and Corporate Customer Concentration—The Perspectives of Financing Constraints and Social Responsibility
Bibliographic record
Abstract
Green technology innovations propel both economic development and environmental sustainability. Exploring the contributing factors to green technology innovations carries important policy implications, but research from the perspective of supply chain relationships has been rare. This paper examines the impact of corporate customer concentration on green technology innovations and explores its influencing mechanisms using the data of Chinese A-share listed companies. The results show that a high customer concentration inhibits the quantity and quality of green technology innovations, a finding that is robust when endogeneity is addressed and when alternative measures and an alternative estimation model are employed. Financing constraints and social responsibility play intermediary roles in the impact of customer concentration on green technology innovations. A high customer concentration tends to increase corporate financing constraints and reduce corporate social responsibility performance, which hinder green technology innovations. The heterogeneity analysis reveals that the inhibitory effect of customer concentration on green technology innovations is less severe in digitally transformed enterprises, mature enterprises, or enterprises with a high level of market power. As this study provides a novel perspective on the contributing factors to corporate green innovations, it offers important policy recommendations.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".