Payment Market and Pricing: A Bibliometric Review
Bibliographic record
Abstract
The objective of this article is to analyze the scientific production about payments market and pricing, identifying the stage of publications, the most productive countries and journals, influential authors, and seminal documents, research clusters, and opportunities for future research. The methodology used to achieve this goal was a bibliometric analysis, using data from the Scopus and Web of Science. With 75% of the published records appearing between 2010 and 2021, this analysis revealed that the field is emergent. The impacts discussed are (i) the growth in the number of papers; (ii) the democratization of access to payment services; (iii) the relevance of pricing in payment processes; and (iv) the agents involved in payments transaction flow. The results show that few studies investigate payments market and pricing, making it an important topic for future research (allied with the identified hot topics) and the major contribution of this paper.
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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 | 0.013 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.092 | 0.112 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".