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Record W3128496335 · doi:10.1155/2021/6699270

Low-Cost Carrier Passengers’ Willingness to Pay for the Seat Preselection Service: A Case Study on the Taiwan-Japan Route

2021· article· en· W3128496335 on OpenAlexvenueno aff
Rong‐Chang Jou, Yi-Chun Chiu, Chung‐Wei Kuo

Bibliographic record

VenueJournal of Advanced Transportation · 2021
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicAviation Industry Analysis and Trends
Canadian institutionsnot available
Fundersnot available
KeywordsLow-cost carrierBusinessPurchasingService (business)TicketAviationPaymentMarketingWillingness to payValue (mathematics)Service qualityAir travelAdvertisingFinanceEconomicsEngineeringComputer scienceComputer security

Abstract

fetched live from OpenAlex

Due to the rising of consumer awareness, consumers not just pay more attention to leisure activities but also demand more on the related service quality. In recent years, low-cost carriers (LCCs) have continued to expand their routes, and traditional aviation is no longer the only choice for travel abroad. Different from the traditional way of operation, LCCs focus on reducing nonessential expenses. The concept of payment by service offers passengers the options for relatively low ticket price. To continuously operate in the highly competitive aviation industry, most airlines have introduced distinctive “value-added additional services” to attract air passengers. This study discusses the seat preselection, value-added service, and behavior of Taiwanese passengers who take low-cost flights to Japan. The results indicate that passengers who have experienced purchasing preselected seats are more willing to purchase this additional service and spend higher amount of money for that service. In addition, the result also indicates that younger people are more willing to accept novel services. These findings could be used as an importance reference for LCCs to guide managerial strategy in the future.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.669
Threshold uncertainty score0.313

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.040
GPT teacher head0.279
Teacher spread0.240 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations12
Published2021
Admission routes1
Has abstractyes

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