E-Payment Acceptance Among German Millennials: Perception and Demographic Background
Bibliographic record
Abstract
During the last twenty years, the growth of the internet and its ability to facilitate e-commerce transactions has had a profound impact on the way business is done by both corporations and individuals (Herhausen et al., 2015; Jeffus et al., 2017). The unprecedented growth of e-commerce has driven the creation of new payment systems to transfer funds electronically from person-to-person or person-to-business (Teoh et al., 2013; Sumanjeet, 2009). The intent of this research is to explore the factors surrounding e-payment use in specific geographical regions to provide a better understanding of that regions e-payment behaviors. This research centers on German millennials, how they value e-payment, and what factors influence their acceptance of electronic funds transfer technology. To reach this goal, a survey was conducted with German millennials exploring what behavior affects their usage of e-payment systems. The results of this survey can provide insights to understand attitudes and behaviors of young generations in their e-payment. The results of this exploratory study center on the descriptive statistics of the respondents.
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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) | 0.000 | 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".