The Impact of Digital Transformation on Corporate Innovation Performance
Bibliographic record
Abstract
In the current context of China's economic transition, focusing on the issue of corporate innovation performance can lay a solid foundation for the acceleration of the digital transformation process as well as the improvement of corporate innovation performance.This paper selects the relevant data of a listed enterprise from 2018 to 2023 as a research sample for empirical analysis.Combined with the DIT model to test the role of digital transformation on innovation performance, and on the two perspectives of financing constraints and intellectual property protection, it specifically studies the mediating effect and adjustment mechanism between digital transformation and enterprise innovation performance.Finally, from the perspective of enterprise heterogeneity (whether stateowned or not, enterprise size, geographical policy), the actual impact of digital transformation on performance under different enterprises is specifically analyzed.The results show that digital transformation has a positive effect on enterprise innovation performance, and digital transformation can reduce financing constraints to a certain extent, ensure sufficient financial support for enterprise operations, and contribute to the improvement of enterprise innovation performance.Research on the moderating mechanism shows that intellectual property rights have a positive impact on digital transformation to promote the enhancement of enterprise innovation performance.Further heterogeneity analysis shows that digital transformation has a more prominent effect on innovation performance in large-scale enterprises.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".