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Record W4388297732 · doi:10.5267/j.uscm.2023.10.015

The role of service quality, facilities, and prices on customer satisfaction in Indonesia aviation in the COVID-19 pandemic

2023· article· en· W4388297732 on OpenAlexvenueno aff
Johar Samosir, Okin Purba, Prasadja Ricardianto, Erman Noor Adi, Ernanto Wibisono, Chatarina Rusmiyati, Trilaksmi Udiati, Andayani Listyawati, Endri Endri

Bibliographic record

VenueUncertain Supply Chain Management · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicConsumer Behavior and Marketing Influence
Canadian institutionsnot available
Fundersnot available
KeywordsCustomer satisfactionStructural equation modelingAviationService qualityBusinessSample (material)Coronavirus disease 2019 (COVID-19)Service (business)Quality (philosophy)MarketingPandemicStatisticsEngineeringMathematics

Abstract

fetched live from OpenAlex

Lion Air is an airline that has a low-cost carrier concept, which makes Lion Air an airline that has low prices. With these low prices, does it improve customer fulfillment? This study aims to determine the effects of service quality, amenities, and pricing on purchase choices. Associative quantitative research methodologies are used in this kind of study. The sample comprised 210 individuals who had taken a flight on Lion Air during COVID-19 and were at least 17 years old. The analysis technique uses Amos 24 software and SEM (Structural Equation Modelling) analysis. The results showed that service quality has a favorable and considerable impact on customer satisfaction. Facilities have a significant and positive effect on customer satisfaction. Price has a positive and substantial influence on customer satisfaction.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.296
Teacher spread0.257 · 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 source (direct Gemma or distilled Codex), 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

Citations6
Published2023
Admission routes1
Has abstractyes

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