Analyzing the Online Reputation and Positioning of Airlines
Bibliographic record
Abstract
The aim of this study is to propose a methodology to define the positioning of airlines in terms of their online reputation measured with quantitative variables and applied in the airline industry. Reviews shared on the Internet give key information about service quality and value as perceived by customers. To carry out the empirical study, we obtained the information available on TripAdvisor about airlines in Europe, the USA, Canada, and other countries in America, differentiating also between airlines that follow a low-cost strategy and those that do not apply it. The results show that there is a significant difference in key service quality variables between airlines in the different geographical areas studied on the one hand, and the low-cost strategy applied on the other. The variables to be used to conduct the positioning analysis in the airlines are determined. They also show that the methodology has relevant practical implications and provides tools to further develop research related to the online reputation and strategic positioning of airlines.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".