Electronic Payment Behaviors of Consumers under Digital Transformation in Finance—A Case Study of Third-Party Payments
Bibliographic record
Abstract
In the digital era, new financial technologies and big data are accelerating the development of financial transactions. With the rise of e-commerce transactions, the financial industry has come to recognize that banking as a service can be seamlessly integrated into any scenario, thanks to disruptive innovation driven by electronic and third-party payments. This study aims to examine the consumer acceptance of third-party payment systems offered by electronic payment platforms for e-commerce, as well as their continued usage in the context of digital transformation in finance. This study employed the questionnaire survey method, and it distributed questionnaires to consumers who have used third-party payment systems. A total of 332 valid questionnaires were collected. The results indicate that user acceptance of innovative technologies and various external variables (e.g., the user’s external environment, internal characteristics, and information system quality) were significantly positively correlated with perceived usefulness, perceived ease of use, and behavioral intention regarding the electronic payment behaviors of consumers. Based on the empirical results, this study proposes important managerial implications for the financial industry and e-commerce platforms in promoting electronic payment innovation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".