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 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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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, unvalidatedMachine predicted; a candidate call from one teacher head, 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".