Perceptions of Consumers in the Airline Industry Using a Qualitative Data Analysis Methodology: An Applied Research under an International Orientation
Bibliographic record
Abstract
Greater digital connectivity, improved practical functionalities of internet and website interface, and the wide embrace of the social-media lifestyle, have induced higher level of activism and participation by the consumer class, as evidenced by the more frequent posting of online customers’ reviews. The textual and qualitative features of online customer reviews can be effectively reviewed and analyzed through the application of robust qualitative data mining methodology and text statistics. Qualitative Data analysis methodology (QDA) greatly helps with the technical steps for the codification, interpretative analysis, hypothesis testing, and predictive iterations. This research uses this methodology and proceeds with an in-depth analysis of N=1222 written reviews to explain the most common complaints of passengers under an international and comparative approach of two airlines in Asia. The conclusions and results of the research are in line with other quantitative research done by (Messner & Wolfang, 2016) stating that Food and Beverages, In-flight entertainment and the quality of the seats in the encounter stage of the service should be the major concerns of today´s airlines.
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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 | 0.014 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".