Peer to Peer Lending Industry in China and Its Implication on Economic Indicators: Testing the Mediating Impact of SMEs Performance
Bibliographic record
Abstract
This papers studies and analyses the development and current status of Financial Technology (FinTech) industry and Peer-to-Peer (P2P) Lending industry in the People’s Republic of China (mainland China), and then investigates the impact of the P2P industry on development of Chinese Small and Medium-sized Enterprises (SMEs) as well as selected economic indicators. Due to the fact that Fintech and more particularly P2P industry is a recent phenomenon, secondary data was collected for 2014 to 2019 from government data sources and company’s websites. The results of Structural Equation Modeling (SEM) analysis indicate the significant impact of P2P lending industry on the economic indicators with mediating impact of SMEs performance. The finding shed light on the financing difficulties faced by SMEs and the role of P2P industry to bridge this gap which have implications for government policies and financial institutions.
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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 | 0.003 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".