Investigating the Factors Influencing the Usage of Smart Entry Service: Incheon International Airport Case Study
Bibliographic record
Abstract
This study seeks to improve our understanding of airport users’ intentions of using the smart entry service (SES) by testing a research model that considers functionality, security, perceived enjoyment, perceived ease-of-use, perceived usefulness, and intention to use simultaneously. The variables that affect the intention of using the SES were investigated, and the correlations among the variables were analyzed. Through the E-Technology Acceptance Model (TAM) that is based on the concept of self-service technology, a research model of the intention of using SES was developed in this study. Surveys were conducted targeting 276 passengers who were experienced with SES, and the correlations among the variables were analyzed using a structural equation modeling. It was found that there were significant relationships between the variables, except in four paths. The result showed that factors such as functionality, security, perceived enjoyment, perceived ease-of-use, and perceived usefulness were confirmed to have positively affected the intention of using SES. The outcomes of this study may be used as baseline data for establishing a strategy to promote the use of the SES.
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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 | 0.002 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".