A user perspective on contrasting factors of contactless mobile payments adoption
Bibliographic record
Abstract
This study investigates the adoption of contactless mobile payments from a user point of view. Near Field Communication (NFC) mobile payments that consist of credit card contactless payments with smartphones are believed to be on a rapid growth path due to expected advantages for all major parties involved: consumers, credit card companies, banks, smartphone manufacturers, and mobile operators. Despite this overall optimistic picture there may, however, be some potential user doubts that would slow adoption. Thus, NFC payments might be associated with both fears linked to credit card use (e.g., account security) and to smartphone use (e.g., privacy). To assess the contrasting, positive and negative, factors of contactless mobile payments adoption from a user perspective, a cross-sectional investigation was run with two samples of Canadian consumers, 150 participants each. While one of the samples was presented information emphasizing the advantages of NFC mobile payments, the other sample was presented information pointing to possible issues associated with this new form of mobile payments. Following that, an online survey was conducted simultaneously with all participants in the two samples. Outcomes indicate that, irrespective of the information offered a priori, consumers perceive both opportunities (e.g., utility and fun) and challenges (e.g., unnecessary complications or privacy threats) associated with the service provided. Major players on the NFC mobile payments market should address these salient consumer factors in order to increase consumer adoption and, hence, the overall success of contactless mobile payments with smartphones.
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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 | 0.004 | 0.014 |
| 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 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".