The Localization Strategies and Success of Costco : Focusing on a Japanese Mature Retail Market
Bibliographic record
Abstract
Purpose - This research addresses the question of how an international retailer like Costco can succeed in a foreign mature market and satisfy the local consumers. Our study aims to promote our understanding of how foreign retailers influence local consumers in a mature market with differentiated business models. Research design, data, and methodology - Our study uses company publications, secondary sources of information and the results of a questionnaire survey consisting of 106 participants. Consumer responses were solicited through a questionnaire survey conducted in the city of Kobe in December of 2013. Results - Product differentiation from local retailers in a mature market like Japan gave Costco a competitive edge. Compared with local supermarkets, Costco was preferred by Japanese consumers for its variety of goods that it carries, as well as in-store promotion large package of selling units, in-store amenities, and customer services. Conclusions - First, a theoretical framework is proposed in this study that can aid in developing localization strategies in a mature market such as Japan. Second, it reveals that an international retailer can succeed in a foreign market by stimulating local consumers to change their purchasing behavior, without having to alter the prevailing format of operation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".