The Impact of Country-of-Origin, Ethnocentrism and Animosity on Product Evaluation: Evidence from Romania
Bibliographic record
Abstract
Purpose: The aim of the research is to identify whether product country image influences consumption patterns and purchase decisions of Romanian consumers, as well as to identify stereotypes regarding foreign products. Furthermore, the study aims to provide clear evidence regarding Romanian consumers’ ethnocentric tendencies and the countries towards which they exhibit animosity feelings. Research Methodology/Approach: Quantitative data collection method applied on Romanian consumers, with a sample consisting of 150 respondents, living in Bucharest, answering a tested self-administered questionnaire based on the CETSCALE. Findings: The results of the research show that country of origin impacts product evaluation, with a significantly high difference between domestic products (Romania) and those from three foreign countries (Russia, Hungary and South Korea). The results suggest that the level of consumer ethnocentrism is low among Romanians, but they do exhibit certain animosity tendencies towards Russia and Hungary with substantive demographic differences identified. Originality/value: The research is the first of its kind conducted among Romanians, adding knowledge to the country-of-origin topic, as well as regarding consumer ethnocentrism and animosity issues. Practical implications: This research is of interest to those looking to export to Romania. It provides clear insight regarding the Romanian consumers’ perceptions regarding foreign products, their ethnocentric tendencies and the potential animosity feelings that they are exhibiting. Furthermore, it offers an useful tool for market segmentation. Keywords: international business, consumer behaviour, country-of-origin, ethnocentrism, 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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| 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".