Determinants of e-payment system perception and satisfaction: Journey from India to Canada
Bibliographic record
Abstract
AbstractCanada is the country, most embracing cashless technology hardly surprising since there are over 2 credit cards for every person living there as claimed by a recent study of forex bonuses 2017. In 2016 this position was attained by Sweden, where barely 1% of the value of all payments was paid in coins or notes. Is India still on the verge of being known as “yet to become a cashless economy”? If yes, then how much time does India expect to become a cashless economy. It is commonly believed that good security gains public trust, and the perceptions of good security and trust ultimately increase the use of electronic payment system. In this paper, we examine the relationship between the security measures offered by the e-payment channel and the ultimate perception of the consumers of the security, trust and usefulness of the e-payment system this study proposes an empirical model that delineates the factors of consumers’ perceived security and perceived trust, with the effects of perceived security and perceived trust on the use of e-payment systems. Primary data has been collected through questionnaires and hypothesis testing is done. Our results show that transaction procedure significantly impacts the perception of the customers which can be employed by the service providers to make e payment sources more efficient and increase its usage.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".