Multiple store brands: An empirical investigation of consumer perception
Bibliographic record
Abstract
For years, supermarkets have been using store brands to attract price-sensitive consumers. Recently, supermarkets have introduced premium store brands in the same product category to compete with national brands. This has not caught the attention of researchers. Thus, the central purpose of this study was to investigate the role of multiple store brands and consumer perception about them. This objective was accomplished by selecting ketchup as a product category and by collecting primary data from shoppers across three supermarket chains in southwestern Ontario. The survey was conducted by shopper intercept method. The results revealed four equal sized segments in the market place: Loyals, Switchers, Price-Hunters, and Indifferents. It was discovered that packaging is an important attribute when buying ketchup; consumers prefer the traditional red color and few are willing to try a different color. The introduction of the premium store brand, in the market brought to supermarkets significant extra market share and it comes mainly from the national brand market share and marginally from its store brand peer. These findings may provide useful information for both supermarkets and manufacturers.
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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 | 0.003 | 0.009 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".