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Record W2980000480 · doi:10.5539/mas.v13n11p37

Modeling Air Travelers' Experience Based on Service Quality Stages Related to Airline and Airports

2019· article· en· W2980000480 on OpenAlexvenueno aff
Claudia Muñoz, Henry Laniado, Jorge E. Córdoba Maquilón

Bibliographic record

VenueModern Applied Science · 2019
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicAviation Industry Analysis and Trends
Canadian institutionsnot available
Fundersnot available
KeywordsService qualityBusinessService (business)MarketingContext (archaeology)Structural equation modelingScale (ratio)Customer satisfactionQuality (philosophy)PerceptionAdvertisingComputer sciencePsychologyGeography

Abstract

fetched live from OpenAlex

This paper proposes a new scale for assessing traveler experience in air travel. Here, passenger experience is measured through travelers' perception of service quality, considering it as a chain of services. The new scale is called air travel service quality (ATSQ). It considers three service quality stages: departure airport service, airline service, and arrival airport service. This paper applies the ATRS scale to examine service quality in domestic travels in a Colombian context. Given that traveler’s experience plays a crucial role in determining passenger’s satisfaction, a structural equation model (SEM) was applied to examine the relationship between service quality stages, customer satisfaction, and behavioral intentions. Adding the passengers' perception of the arrival airport to the integrated service quality measurement is considering one of the main contributions of this study. The finding of this research confirmed that all three stages of air travel service have a significant, positive effect on passenger satisfaction. The scale found in this research should provide useful information for developing effective operational and marketing strategies for the air travel market. In this way, airports and airlines could better understand how traveler’s perception of service quality may affect each choice related to which departure airport, airline, and arrival airport combination to choose from.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.131
Threshold uncertainty score0.795

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 head0.061
GPT teacher head0.282
Teacher spread0.222 · 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 designSimulation or modeling
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

Citations11
Published2019
Admission routes1
Has abstractyes

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