Export marketing in higher education: an international comparison
Bibliographic record
Abstract
Purpose How higher education institutions (HEIs) approach the recruitment of international students is an area of global interest (James-MacEachern, 2018, Ross et al., 2013), but there is limited focus on how institutions in different parts of the world approach international student recruitment as an export marketing orientation (EMO). The purpose of this paper is to examine the similarities and differences of export marketing orientation amongst three higher education institutions. Design/methodology/approach This study uses export marketing concepts to compare three universities from Canada, Hong Kong and the UK to explore how institutions use international student recruitment as export marketing in international markets. Findings The study finds a number of similarities and differences in how HEIs react and respond to market and global environments, and responses impact the level of EMO. It argues that institutions rely differently on export marketing in their approach international students and highlights the need to understand how various factors such as national policy and institutional strategy impacts institutional adoption of an EMO in higher education. Originality/value By comparing HEIs from different parts of the world, this paper shows differences in export marketing orientation that are shaped by national policy frameworks and organizational culture. This is the first time three institutions from Canada, Hong Kong and the UK have been compared for EMO, and this study provides new insights into the factors that contribute or hinder EMO for HEIs.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".