Security of Personal Information in Electronic Payments: A Bibliometric Analysis and Model Extension
Bibliographic record
Abstract
The global rise in the use of electronic payments highlights the growing importance of personal information security in this domain.Scholars have explored various aspects of this issue, including payment systems, technological implementations, user acceptance, behavior, and psychological factors.This study aims to identify research gaps and future trends in personal information security in electronic payments through bibliometric analysis.Data was sourced from the Scopus and Web of Science databases, covering the period from 1974 to 2023.The dataset was analyzed using VOSviewer and R-Bibliometrix (Biblioshiny) software.The co-occurrence network output indicates that future research should focus on clusters with lower density and central positions, such as user acceptance of new technologies, consumer behavior, user trust, and user satisfaction with electronic payments.The R-Bibliometrix (Biblioshiny) thematic map identified research gaps across four quadrants: motor themes, niche themes, emerging or declining themes, and basic themes.The study proposes potential research areas in personal information security in electronic payments and develops an extended UTAUT (Unified Theory of Acceptance and Use of Technology) model tailored to this field.The study identifies trust and technology acceptance as critical themes and extends the UTAUT model to incorporate personal information security cognition.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.020 | 0.011 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.003 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".