Bibliographic record
Abstract
Purpose The aim of this paper is to propose an approach to international market segmentation that identifies meaningful cross‐national consumer segments, which focuses on airline passengers in the NAFTA market. Design/methodology/approach A conjoint analysis is used to evaluate consumers' preferences for six flight attributes: price, in‐flight service, number of stops before destination, on‐time performance, frequent flyer programme, and country of airline. A cluster analysis based on the relative importance scores of each of the six flights attributes then identifies five segments that prioritize similar product attributes within each country. Findings A representative sample of 4,787 airline passengers from the three countries reveal that price is the most important attribute for consumers from the USA and Canada, while on‐time performance is the most important attribute for Mexican consumers. A cluster analysis identifies five segments that prioritize similar product attributes within each country. It is also found that there are five cross‐national consumer segments in the NAFTA market that are homogeneous in terms of consumer preferences but heterogeneous in terms of relative group size and demographic variables. Research limitations/implications The study is based on a purposive sample, which limits the ability to generalize to the whole population with any known degree of precision. Practical implications The research produces practical operational information on each segment that is translatable into strategy, specifically in terms of positioning, promotion, and targeting of the airline service. Originality/value The paper sheds light on the nature of cross‐national segmentation in the NAFTA air passengers market and the resulting cross‐national segmentation will be highly relevant for international marketing management.
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 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".