Coach Versus Goldlion: The Effect of Socially Versus Personally Oriented Motives on Consumer Preference for Foreign and Domestic Masstige Brands in Emerging Markets
Bibliographic record
Abstract
The fast-growing middle class in emerging markets is leading luxury brands to adopt an affordable luxury (aka “masstige”) approach to reach the mass market. However, academic research in this promising area is scarce. Focusing on China, the world's largest emerging market, this research shows a novel pattern of masstige brand consumption: Instead of always preferring foreign masstige brands (e.g., Coach) as implied in previous international marketing literature, consumers in emerging markets prefer domestic masstige brands (e.g., Goldlion) when their personally oriented motives are made salient. In contrast, consumers prefer foreign masstige brands when their socially oriented motives are made salient. This stems from domestic masstige brands better manifesting self-focused intangible attributes (i.e., the actual value to please oneself), whereas foreign masstige brands are superior in other-focused intangible attributes (i.e., the symbolic value to impress others). Three studies using a multimethod approach provide converging results that support this phenomenon. The findings bring significant contributions to the literature and offer actionable implications for managers, including positioning and price promotion strategies for masstige brands in emerging 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".