Bibliographic record
Abstract
To provide passengers with a better flight experience and make their journey more comfortable, airlines are diversifying their services to attract more customers. One of these services includes the development of a sleeper seat. A sleeper seat, also known as family couch in Taiwan, mainly provides a row of 3–4 economy class seats that allow passengers to lie down. Although Taiwan’s China Airlines originally offered this service, it stopped due to poor sales; however, other international airlines continue to implement similar services. Regarding previous research exploring air service issues, almost no research has focused on sleeper seats. Accordingly, this article focuses on exploring Taiwanese passengers’ willingness to pay (WTP) for the sleeper seat service and the influencing factors. The contingent valuation method (CVM) was used to construct the price scenario of passengers’ WTP. To avoid estimation bias and thus provide more reliable results, the spike model was used to estimate passengers’ WTP for the sleeper seat service and the influencing factors. According to the results, when considering multiple variables, people were willing to increase the price paid to use parent-child cabins by NTD 11,194, which was about 75% of the original price but lower than the preferential price offered by the airlines, indicating a gap between the amount people were willing to pay and the airlines’ pricing. In addition, if passengers were traveling with children or if the passengers had higher flight frequencies and personal incomes, they were willing to purchase the sleeper seat service. However, people who had not experienced the sleeper seat service were unwilling to purchase it. This paper is practical to reflect current aviation industry marketing, and the research results contribute to the existing literature and provide a reference for Taiwan’s civil aviation industry when relaunching the sleeper seat service in the future.
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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 | 0.002 |
| 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 | 0.000 |
| 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".