Bibliographic record
Abstract
With the evolution and progression of fintech, an increasing number of enterprises have introduced fintech. However, it is still unclear whether fintech can improve enterprise value in practical application. This study collected relevant data of Shanghai and Shenzhen A-share listed companies in China from 2011 to 2019, and analyze the objective impact of fintech on enterprise value by linear regression. Linear regression model was established, descriptive statistics were performed, collinearity problem was excluded, and significance level was observed. By conducting rigorous empirical research to explore the use of fintech on the value of enterprises. Through this research, it is found that the use of fintech has a significant improvement on the value of enterprises, indicating that fintech is conducive to improving the efficiency of business operations and improving their performance in the market. Further research finds that the significance level of small enterprises is significantly positive at the 5% level. It is speculated that this is because for small enterprises, the technology level is relatively backward compared with that of large enterprises. At this time, the introduction of fintech will greatly promote the development and value of enterprises. Finally, it is concluded that relevant enterprises can improve their self-value by introducing fintech.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".