Determinants of store brands’ success: a cross-store format comparative analysis
Bibliographic record
Abstract
Purpose – The purpose of this paper is to focus in customer-based store brand value by comparing three different retailing formats – supermarkets, hypermarkets and discounters – in order to assess how store brand value stems from and to understand the store format influence. Design/methodology/approach – Respondents were randomly selected and data were collected using an on-line structured questionnaire, focussing on Spanish large retailers. Then, hypotheses were tested performing structural equation modeling. Findings – The results suggest that perceived quality, price image along with store commercial image have significant positive influence on store brand value and purchase intent. Moreover, store brands’ performance in the marketplace depends on different variables across the analyzed retailing formats. Research limitations/implications – These variables may be managed by retailers in order to enhance their own brands’ value proposition. These research implications should be considered within the context of a geographical limitation, despite providing the basis for further research on the topic. Originality/value – The study adds to the growing literature in retailing a cross-store format comparative analysis, remaining a deeper understanding on how store brands create value from the consumers’ standpoint, based on an empirical research in a European developed market.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".