Bibliographic record
Abstract
This paper asks whether brand extension can serve as a signal of product quality given that it costs less than a new brand. (Existing literature has assumed either that brand extension is cost-neutral or that it costs more.) I show that it can as a perfect Bayesian equilibrium, but the argument is unconvincing. For one thing, the separating equilibrium is not unique; a pooling equilibrium also exists in which brand extension signals nothing. For another, the separating equilibrium relies on off-equilibrium beliefs that are poorly motivated in the model. I propose a refinement of the perfect Bayesian equilibrium that resolves both issues. Empirical off-equilibrium beliefs require that consumers' off-equilibrium beliefs be justifiable on the basis of their prior beliefs and product performance observations. With empirical off-equilibrium beliefs, two necessary conditions for brand extension to signal product quality are identified: (i) consumers must perceive old and new products of the firm to be positively correlated in quality, and (ii) at least some consumers must identify with brands and not the firm behind the brands. Even with these conditions in place, the signaling argument is fragile: firm observability of past performance diminishes brand extension's signaling capability; an arbitrarily small probability of failure for good products eliminates it. My results suggest that, going forward, the case for brand extension must rest on foundations other than signaling product quality.
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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 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".