Fit or signal: A consumer's evaluation of brand alliance
Bibliographic record
Abstract
In an alliance involving two or more brands, there are issues of general fit of the partnering brands that might signal positive or negative effects. The need to assess the signal or fit of co-branding partners in an alliance is central in this study. Consistent with previous research in brand alliance, the evaluation of co-branding alliance was modeled as a reflection of (1) the fit (quality sense) of each of the constituent brands with the co-branded product, (2) brand function (signal) of each of the brands involved in the extension and (3) the choice preference of consumers. The study adopted a computer based questionnaire of mall shoppers in Toronto (sample = 202). Using a multi-method analysis of regression, best-worse and discrete choice models, the signal or fit was determined by considering simultaneously the brand (signal) and quality (fit) of each of the constituent brands with respective products featured as the extension. The findings suggest that fit model is preferred in rating data and discrete choice situations while signal model works better in a Best/Worse situation. This finding will be useful to marketing and brand managers, as well as research in brand alliance in understanding processing and decision making.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".