A study of ethnic perception gap on consumer boycott of Korean and Canadian University students
Bibliographic record
Abstract
Purpose In this paper, the authors aim to offer a cross-cultural comparison of the boycott intentions of university students in Canada with those of students in Korea. Design/methodology/approach The data were collected from students at Inje University and York University via self-administered questionnaire. A t-test found that Canadian students’ answers showed significantly greater scores in ethnocentrism, boycott attitudes prior to reading the target article and motivations related to self-enhancement compared to those acquired from Korean students. However, the motivation of counterarguments and the boycott intentions of Korean students’ toward Rogers, the parent company of Maclean’s magazine, showed significantly higher scores than those gained from Canadian students. Findings The boycott case used in the study is Maclean’s magazine, a Canadian news magazine, which published a controversial article called, “Too Asian? Some frosh don’t want to study at an “Asian” University”. A noticeable gap in each group of students’ boycott attitude and intentions toward Rogers, the parent company of Maclean’s magazine was found. Originality/value In the multiple regression analysis, the boycott motivation of self-enhancement was the most influential variable on boycott intentions. The boycott case examined in this paper is a practical case study of cross-national grouping as well as the perceptional difference of the locus of corporate accountability that comes from cross-cultural backgrounds.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".