Store image and layout design implementation in developing male cosmetics and perfumery stores in the Netherlands
Bibliographic record
Abstract
Together with the emerging male grooming trend in various countries all over the world, the growth of male cosmetics in the Netherlands also experiences a considerable improvement since the past several years. In response to that, the cosmetics retailers should pay more attention on the unfulfilled demand of the male consumers in terms of the retail setting which are comfortable for them to shop in. Through this report, the researcher will explain about the emerging male cosmetics market, which has became a good opportunity that can be utilized by the cosmetics retailers in the Netherlands to serve the new market. This report also contains the research methodology that is used by the researcher to collect the primary and secondary data, the findings, and the conclusion of the research. The suggestion for the cosmetics retailers are begun by analyzing the gender role orientation, shopping behavior and preferences, as well as the expectation of the consumers toward the male cosmetics store in the future. Several conclusions are draw from the analysis done by the researcher, and are converted into a set of useful suggestion regarding the implementation of store image and the appropriate store layout designs to be used in order to attract male customers to come and shop in. At the end of the report, the limitations of the research are discussed in order to generate the necessary recommendation for further research.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".