The Moderating Role of Involvement and Differentiation in the Evaluation of Brand Extensions
Bibliographic record
Abstract
Two experiments qualify the previously observed finding that a moderately incongruent brand extension is evaluated more favorably than a congruent or extremely incongruent brand extension and reconcile this finding with other outcomes that have been reported in the brand extension literature. A congruent brand extension is judged more favorably than either a moderately incongruent extension or an extremely incongruent extension when involvement in the task is low. Apparently, incongruity per se does not always prompt the elaboration required to reconcile a moderately incongruent extension with the parent brand and, thereby, enhance evaluation of the moderately incongruent extension. Further, when involvement is high, a moderately incongruent brand extension may only be judged more favorably than a congruent one if the extension is undifferentiated. If the extension is differentiated, the differentiation may provide a basis for favorable evaluation irrespective of the level of congruity with the brand. Recall of information about the performance of the extension relative to competitive brands and measures of attitude toward the parent brand, fit between the extension and the parent brand, and task satisfaction provide insight into the processes that underlie these effects.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".