Digital Nudging for Accelerating the Take Up of Electronic Payment in the Moroccan Port Ecosystem
Bibliographic record
Abstract
In Morocco's port and foreign trade ecosystem, new electronic payment methods, such as online payments, have been introduced by public decision makers to speed up the payment process and thus the transit and clearance of goods.Despite their variety and availability, as well as their increased security, these new payment methods are not yet fully accepted by users in the port community, which makes the use of checks and cash still one of the most used payment methods.The proposed approach aims to encourage customers to use electronic payment methods in an ecosystem dominated by traditional payment methods by designing adaptive nudges from the three main domains: artificial intelligence, humancomputer interaction, and user experience, in order to support this strategic decision towards electronic payment adoption.In fact, several studies have consistently shown that digital nudges are effective strategies for leveraging cognitive biases to positively influence user behavior and decision-making (Behavioral improvements ranging from 10% to 30% depending on the context and type of nudge).This article provides an illustration of the implementation of digital nudges through a well-defined process, drawing on evidence from other studies that have demonstrated the effectiveness of nudging.This approach is expected to influence user decisions in digital environments, particularly in the adoption of electronic payment methods, with the objective of achieving behavioral change of up to 30%.These insights offer a valuable foundation for researchers and practitioners focused on studying or designing information systems and interventions that support user decisionmaking in digital environments.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".