Does the Endorser’s Nationality Matter? An Investigation of Young Taiwanese Consumers’ Selecting Smartphone
Bibliographic record
Abstract
The use of a wrong celebrity endorser may boost the animosity of local consumers toward a specific country with a complex consumer animosity. Thus, this study aims to examine whether the match or mismatch between the endorser’s nationality and the brand’s country of origin affects consumers’ purchase intention. We use an experimental study to investigate the purchase intention of young Taiwanese consumers toward Korean smartphone brand “Samsung”. Results indicate that the endorser’s nationality does matter. While celebrity endorsement has a consistent positive effect on purchase intention, the effect of the endorser’s nationality varies. Specifically, using domestic celebrity as an endorser has more advantage in enhancing the purchase intentions of consumers who are neutral and dislike Korea than using a foreign celebrity. However, an endorser’s nationality has no significant difference among young Taiwanese consumers who like the Korean culture. Smartphone producers such as Samsung and LG can use the results to promote their new products in a foreign country such as Taiwan and Japan with a complex consumer animosity.
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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 | 0.002 | 0.006 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".