French Canada and the Philippines: Comparing Product-Country Perceptions
Bibliographic record
Abstract
This article presents the results of a survey of 195 Philippine and 250 French Canadian male consumers in which the perceptions of thirteen countries of origin (COO) were measured in a multi-attribute/multi-dimensional context. The study was carried out in order to broaden the conceptual underpinnings of COO effects that have so far been derived mainly from Western studies by providing evidence of the moderating effect of nationality. In comparison with the French Canadians, Philippine respondents were more favorable towards products made in highly industrialized nations and products designed in East Asian countries. They also showed a greater home-country bias than their counterparts and were much more familiar with products made in ASEAN countries. In general, Canadian products were perceived to be better performing, of higher quality, more original, and more expensive than Philippine products. In evaluating products, the brand name and the country where the parts originated from were more important to Philippine consumers, whereas country of design, country of assembly, and warranty were more important to French Canadians. Younger and less affluent French Canadians were more favorable towards ASEAN products whereas educated French Canadians were more favorable towards products made in highly industrialised countries. While the most important predictor of country perceptions in the French Canadian sample was involvement with automobiles, in the Philippine sample it was involvement with VCR.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".