Enhancing E-Commerce Processes with Alerts for Credit Card Payment
Bibliographic record
Abstract
With keen competition and the continuous quest for service improvement, e-commerce processes are becoming increasingly complex. Recent adoption of the Service-Oriented Architecture has further facilitated cross-organizational process enactment and enabled e-commerce enhancement. Despite a customer interacting with one website, multiple parties are actually involved at the backend such as logistics, services, and payment. As the payment process is indispensable for transactions, the authors choose this as the case study. To enhance the security of the payment process, credit card providers have already been using secure processing services to encrypt the credit card information. But if an unauthorized person knows the credit card information, they can still perform any payment illegally. To address this problem, the authors design a Notified Credit Card Payment System (NCCPS) to handle the notification and confirmation process enhancement. Through an Alert Management system component, the NCCPS systematically integrates the communication between merchants, banks or credit card service providers, and mobile service providers by the means of Web services and SMS technologies. The NCCPS also integrates with the customer service call center for the cancellation processes and exception handling. The authors demonstrate the effectiveness of the use of Web services and alerts in e-Commerce and process integration.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".