A Review of the Impact of Third-party Payment on Chinese Residents’ Consumption
Bibliographic record
Abstract
The progressive emergence of different third-party payment systems in the recent past has increased transaction convenience, reduced costs, lowered financial restraints, and simplified access to one's money.The research on thirdparty payment and consumer behaviour has attracted more and more attention.Based on the current situation of the influence of third-party payment methods on Chinese residents' consumption, combined with existing research literature, this paper has sorted out the research results of third-party payment, third-party payment and residents' consumption research results.The results show that third-party payment methods such as WeChat and Alipay have significantly increased the consumption rate of some Chinese residents, mainly related to consumers' perception of increased security, loan choice and convenience.However, third-party payment methods have also exposed consumers to severe risks such as money loss with the ever-increasing issues of hacking, identity theft, fraud schemes, and scamming.They also increase spending habits while reducing saving tendencies, making them less appealing.A discussion and analysis of the impacts of third-party payment methods on the consumption of Chinese residents show they generally increase expenditure.The downside of the E-payment methods, like losing money via hacking, can be reduced with improvements to the systems to increase consumption rates.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".