The Impact of Brand Transposition Strategies and Firm Type on Consumer Ratings of Brand: An Analytical Study of Cosmetic Brands
Bibliographic record
Abstract
Selecting an appropriate brand transposition strategy across different language systems is crucial to a brand’s success in the multi-cultural global marketplace. Drawing upon appraisal theory and using a large data set of 3,100 real cosmetic brands and their consumer ratings in China, we analyze the effectiveness of three common brand transposition strategies across three types of firms. The results show that the brand transposition strategies differ in their effects on consumer ratings of brand, and the effects depend on the firm type (foreign, joint venture, and domestic firms). Specifically, a semantic brand transposition strategy exerts positive effects on consumer ratings of foreign brands and joint venture brands but not of domestic brands. The phonetic transposition strategy and the phonosemantic transposition strategy benefit only foreign brands, and not joint venture brands or domestic brands. To better understand brand transposition between two language systems, we also provide descriptive analyses of the transposing patterns of Chinese character usage across firm types as well as the most frequently used Chinese characters in cosmetic brand names.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".