The image of countries and their products at the subcultural level : investigating differences between English and French Canadian consumers
Bibliographic record
Abstract
Research on the Product and Country Images (PCI) issue (also known as "country-of-origin", or "made-in"), began 30 years ago and has grown rapidly to become one of the most important fields in international marketing and business theory, with over 500 published studies to date. This substantial literature reflects the pervasive presence of origin cues in society and the economy, in public policy and business decision-making. While the body of work on (PCI) has made significant theoretical and practical contributions, numerous unresolved questions remain. Among them is whether the effects of the product-country image vary across subcultural groups within nations concerning products from various countries. Thus, the objective of this study was to determine if consumer attitudes towards foreign countries and their products vary among ethnic subcultural groups (English versus French-Canadians). The data analysis, using multivariate analysis of variance and factor analysis confirmed that subcultural differences between English and French Canadians do indeed exist in the evaluation of foreign countries and their products. Manova tests revealed that significant differences between the two subcultures were detected for eleven of the eighteen countries under investigation. Furthermore, a subcultural bias was demonstrated by the fact that English Canadians gave more favorable scores to products from Great Britain than French Canadians did while French Canadians gave better scores to products from France.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 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".