Bibliographic record
Abstract
A sizable research stream in marketing finds that a strong fit between a brand extension product and its parent brand encourages positive consumer responses. Yet this large body of literature fails to provide managers with specific practical guidance about how to create brand-extension fit for optimal results. The problem is a lack of understanding of what brand-extension fit really is, and there has been little work to address this issue by synthesizing the key dimensions of brand-extension fit. The current article addresses this gap by identifying the key constituent dimensions of brand-extension fit. This is an important topic because brand extensions are essential for business renewal and growth. We identify six dimensions of brand-extension fit: feature-based, function-based, resource-based, usage-occasion-based, market-based, and image-based fit. Each dimension addresses a different aspect of brand-extension fit and suggests ways for brand managers to create brand-extension fit. Less expected is that studies that use a strict subset of these dimensions overweight those fit dimensions that are included, and the associated estimated coefficients are biased. From a managerial perspective, counterfactual analysis also shows that reliance on a strict subset of these dimensions results in suboptimal decisions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".