Attenuating double jeopardy of negative country of origin effects and latecomer brand
Bibliographic record
Abstract
Purpose – The purpose of this paper is to examine the role of ethnocentrism in attenuating the negative country of origin effect and latecomer brands. The literature has established the importance of the “country of origin” effect, and this study compares consumers in the Asian emerging markets to developed consumers' response to cars from China, India and Russia. Design/methodology/approach – Data on consumers' willingness to purchase cars from emerging markets such as China, India and Russia were collected from 3,201 respondents in those three emerging markets and in the three most important Western car markets, the USA, the UK and Germany. The study employed a choice-based conjoint analysis. Findings – The results of this study confirmed the hypothesised ethnocentrism in the emerging markets with a strong preference for their own latecomer brands (Great Wall, Tata and AvtoVAZ, respectively). Developed markets in contrast are more sceptical of the Chinese, Indian and Russian car brands, but there is nonetheless substantial potential, especially with consumers who have previously bought latecomer brands from Asia. Utility values per brand, price, brand-partnership, product features, warranties and also place of manufacturing/assembly have been calculated in the study. Originality/value – This paper should prove valuable to academic researchers in establishing strong consumer preferences in emerging markets for their own products, and in establishing the potential of latecomer brands in developed markets.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".