Bibliographic record
Abstract
This article is based on a study that aims to analyze factors influencing the acceptance of contactless payment devices by customers in Germany. Its purpose is to explain the influence of similar technologies, already in use towards the acceptance of contactless payment technology. Smartphones, especially the I-phone as one device of mobile technology, is offering a mobile payment procedure named Apple Pay. Contactless payment technologies are developing away from physically present credit card shaped plastic cards by an integration into mobile phone devices. Interviews in personal contact on petrol stations in Hamburg, Germany, have been conducted. Petrol stations are typically points of sale with a high rate of non-cash payments and customers have time to answer questions for an interview during refuting. 48 hours of interviews have been collected on two petrol stations in one of Germany’s largest cities, interviewing typical business-customers on a Monday and private customers on the weekend. The technology acceptance model has been chosen to identify and explain the interaction in the customer’s perception of new payment devices. The study reveals that the influence of information on the new payment process and possession of a customer loyalty card are critical variables in the customer acceptance of payment devices. Information about the new payment method proves to be the argument with strongest impact on the acceptance by customers. Besides these variables, perceived usefulness of the technology has been found as a significant factor in affecting the acceptance of contactless payment processes. Apples I-Phone with its current market penetration has the potential to provide a widespread and well known concept as basis for contactless payment technology.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.004 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".