Global vs local brands: how home country bias and price differences impact brand evaluations
Bibliographic record
Abstract
Purpose – The purpose of this paper is to re-conceptualize the distinction between global and local brands, providing a more comprehensive framework, which considers both geographical distribution and ownership. It examines main and interactive effects of consumers’ perceptions of these factors, and studies how ethnocentrism (CET) and price affect brand evaluations, considering a range of price difference thresholds. Design/methodology/approach – A preliminary study ( n =243) examined main and interaction effects of brand globalness and ownership on consumers’ brand quality attitudes and purchase intentions in four different product categories. The main study ( n =558) further explored brand ownership effects by examining the interaction of CET and price differences. Findings – The preliminary study confirmed the distinctiveness of brand globalness and ownership. Consumers evaluated global (vs non-global) brands more positively, regardless of brand ownership (local vs foreign). The main study found that effects of price and CET varied considerably across product categories. Research limitations/implications – Limitations include the use of student samples from a single country (Thailand), and of scenarios instead of real life purchase decisions. Practical implications – The findings suggest that perceived brand globalness positively impacts brand evaluations. Companies may cultivate a global brand image by emphasizing global cues. Local origin allows (global) brands to command a price premium, although this varies across product categories. An emphasis on globalness seems valuable, especially for local brands. Originality/value – This research offers a refined conceptualization of brand globalness, a key construct in international marketing. Additional value is provided by studying price effects, which have received limited attention in international marketing, and substantial data collection (total N >800) in an understudied yet important economy (Thailand).
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".