National versus store brand effects on consumer evaluation of a garment
Bibliographic record
Abstract
Purpose The study reported in this article aims to examine the effects of national versus store brands on consumer evaluation of a garment, taking into account the intended product usage situation (for everyday use versus for a special occasion) as well as price (regular versus discount), type of store (department versus boutique), and store image (lower‐class versus upper‐class). Design/methodology/approach An experiment was conducted with 127 Canadian adult consumers where the above variables were manipulated by means of short vignettes presenting a to‐be‐evaluated branded shirt. Findings The results of the experimental study showed that consumer evaluations of store brands and national brands were influenced by the joint effects of store image and intended usage situation. Practical implications Retailers in upper‐class retail clothing stores willing to promote their store brands should emphasize in their communication programs buying contexts in which an item is needed for some special event (e.g. a wedding anniversary) because this appears to correspond to situations where store brands are best valued. Retailers in lower‐class stores should rather promote their store brands by stressing the good quality of their clothes in day‐to‐day usage situations. As for national brands of clothing, emphasizing the satisfaction guarantee that comes automatically with well‐established brands would seem to be the best communication strategy. Originality/value The paper shows the influence of store image on consumer evaluations of garments.
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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 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".