Poland–Turkey Comparison of Mobile Payments Quality in Pandemic Time
Bibliographic record
Abstract
The main objective of this article is to identify and analyze the use of mobile payments in two countries, Poland and Turkey. The data for the study were collected with the application of the CAWI method in March 2021. The survey covered nearly 650 respondents in total. The basis for comparisons was populations from two culturally distinct countries, Poland and Turkey, which are at a similar level of development as regards the use of the Internet. The studies were carried out simultaneously in both countries and examined the group of young people aged 18–25. The research surveyed the population, which included the most active Internet users who are taking full advantage of the benefits of globalization, which is facilitated by the development of the Internet worldwide. The survey was translated into the respondents’ native languages, initially validated during the pilot studies and then distributed and circulated among the study participants. The obtained findings were subject to comparison, and the differences between the samples were analyzed and commented on to verify the hypotheses formulated in the study. The main limitation of the conducted study was the selection of a random group—the research sample consisted only of members of the academic community. The study presented in the article fills the research gap regarding international comparisons of the use of m-payments in the period of the COVID-19 pandemic. The obtained results indicate the undoubted fact of increased interest in the use of m-payments in e-commerce and e-banking, and even more importantly, differences concerning 40% of the criteria/attributes applied to assess the use of m-payments in both countries. The findings can be used by business practitioners dealing with the development of m-payments. Another potential application is to attempt to bridge the gaps between countries, which may be supported by globalization processes.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".