Does digital finance foster corporate innovation? Evidence from China
Bibliographic record
Abstract
This study examines how digital finance foster corporate innovation. Unlike prior research focused on statistical associations, we investigate the underlying economic mechanisms driving that relationship. Using panel data of Chinese listed firms from 2013–2023, we show that digital finance stimulates corporate innovation through two primary channels. On the supply side, digital finance supports innovation by optimizing resource allocation, improving risk management, and providing high-quality information. On the demand side, digital finance stimulates the innovation needs of enterprises by improving their dynamic capabilities. Moreover, a favorable governance environment further amplifies these effects. Heterogeneity analysis reveals stronger impacts for large firms, firms with IT-background CEOs, capital-intensive and high-pollution industries. Regionally, digital finance plays a more inclusive role in low marketization and underdeveloped areas. Overall, this study systematically uncovers the mechanisms through which digital finance influences corporate innovation and provides novel insights for designing fintech-driven development policies in emerging markets.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".