Which Factors Affect Passengers' Intention to Use the Automated Immigration Clearance System (e-Gate)?
Bibliographic record
Abstract
This study discusses the factors that affect passengers’ intent to use automated immigration clearance system (e-gate) and the corresponding causal relationship. Based on the Technology Acceptance Model, we constructed a model by considering the need for personal interaction and perceived risk, designed questionnaires by focus group discussions and observed and compared passengers’ behaviour by Structural Equation Modeling and Hierarchical Regression to identify several important issues. Results indicated that (1) for experienced users, use attitude and perceived ease of use are the key factors with positive effects on use intention; (2) for inexperienced users, the need for personal interaction negatively influences use intention, and perceived usefulness has little effect on use intention; and (3) ‘experience’ has a significant main effect and moderator effect on the influences of personal interaction and perceived risk on use intention. These results suggest the following important implications: enhanced experience, inductive promotional strategies, positive feedback loop, provide options for immigration examination stamps, and reach a balance between control and facilitation.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".