Fashion retailers rolling out across multi‐cultural Europe
Bibliographic record
Abstract
Purpose Many retailers are expanding throughout Europe, while it is well‐known that large differences still exist between the European countries. This paper aims to explore to what extent the historical expansion sequence patterns of retailers operating across Europe are driven by cultural factors. Design/methodology/approach The paper derives a cultural map of Western Europe based on data of Hofstede and Hall. Three important cultural clusters are identified. Next, this study investigates the expansion sequences of nine big EU‐ and US‐based fashion‐clothing retailers across those three cultural clusters. Findings The results show that initial expansion typically takes place in a neighbor country belonging to the same cultural cluster. Subsequent expansion tends to follow a stepwise cluster‐by‐cluster pattern, where retailers make cluster jumps, first expanding in the same cluster, but already move to another before the first is completed. Practical implications For US/Canada‐based retailers as well as for European‐based retailers it is crucial to fully recognize the differences between European countries, but it is very useful to consider their similarities too. Dividing the European market into clusters of countries seems to be a pragmatic way of handling differences and similarities. This information can help managers to make better decisions on entry sequences in foreign markets. Originality/value To the authors' best knowledge, this is the first study analyzing the complete international entry sequences, i.e. both the initial and subsequent entries of retailers in Western Europe, from a national cultural perspective.
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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 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".