Constructing Marketing Indicators and Measuring the Satisfaction of Asian International Students in the Higher Education Sector
Bibliographic record
Abstract
<p>This paper addresses the construction of marketing mix strategies within the Taiwanese higher education sector and the applicability of such strategies to measure the satisfaction levels of Asian international students. Due to a declining birth rate in Asia as well as an oversupply of schools, recruitting international students is an effective tactic for higher education managers in Taiwan. To pool a representative sample of the population, international subjects were drawn from all higher education institutions in Taiwan. For this research, the author collected 328 valid questionnaires. Descriptive statistics indicated that the seven-factor model was of good fit and included attributes of product, place, price, promotion, people, reputation, and physical evidence. The results from the study highlighted the applicability of the importance-performance analysis (hereafter IPA) for managers attempting to improve their marketing mix strategies and resources from appropriate marketing sectors.</p>
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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 | 0.003 | 0.000 |
| 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".