A Study on the Awareness and Consideration Sets in Relation to Employers in Air Transportation Sector
Bibliographic record
Abstract
This study identifies the awareness and consideration sets for the aviation companies operating in different fields of air transportation and the factors influencing the sizes of such sets. A two-stage study was conducted with the students from the Aviation Management and Civil Air Transportation Management Departments of Turkish universities through online survey. First, the awareness sets for five different types of aviation companies in Turkey were formed. Then, the consideration set was formed by listing and asking the students to specify the companies that they might want to work with. It is found that potential employers for air transportation companies, have a more favorable opinion of the airline companies and the awareness set size is the greatest for this company type. Significant relationships between both the internship status and education program of the students and the awareness set size is identified. Top five companies in the consideration are identified as airlines and airport/terminal companies. It is found that the size of the consideration set differed based on the education program of students. The study is a pioneer in applying the set theory to employer branding context and investigating the awareness and consideration sets for the employers in air transportation industry.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".