Market segmentation in the cell phone market among adolescents and young adults
Bibliographic record
Abstract
Purpose – The purpose of this study is to investigate the existence of inter-market market segments in the adolescents' and young adults' cell phone product-market in Finland, United Arab Emirates, Canada, China, and New Zealand. Drawing upon cell phone feature preferences criteria cited by Işıklar and Buyuközkan, the existence of inter-market market segments using these feature preferences as the cluster variate was examined. Design/methodology/approach – Using a survey questionnaire, data was gathered from 403 high school and 892 undergraduate students in Finland, UAE, China, Canada and New Zealand. Findings – The results of the study suggest the inter-market market segments do exist in the countries of this study, but their existence varies to some degree by country. Originality/value – An important implication of the research is the existence of the five inter-market segments among the adolescents and young adults in the five countries was established. Consequently, the inter-market segments extend over the borders. The five inter-market segments exist in all country markets except in New Zealand, which included only four segments. These five segments also appear to be unique and large enough in size, which are the key requirements in terms of successful segmentation, and thus warrant the development of unique products, services and marketing programs for the segments.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".