Fashion retailers rolling out across multi‐cultural Europe
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it