Brand Effects on Choice and Choice Set Formation Under Uncertainty
Bibliographic record
Abstract
This paper examines the effects of brand credibility, a central concept in information economics–based approaches to brand effects and brand equity, on consumer choice and choice set formation. We investigate the mechanisms through which credibility effects materialize, namely, through perceived quality, perceived risk, and information costs saved. The credibility of a brand as a signal is defined as the believability of the product position information contained in a brand, which depends on consumer perceptions of the willingness and ability of firms to deliver what they have promised. The choice set is defined as the collection of brands that have a nonzero probability of being chosen among those actually available for choice in a given context. Furthermore, we study the impact of brand credibility on the variance of the stochastic component of utility. Not only do choice model parameters capture the impact of systematic utility differences on choice probabilities, but also the magnitude of this systematic impact is moderated by the relative importance of the stochastic utility component in preference. We term this moderation phenomenon preference discrimination, which we conceptualize as the decision makers' capacity to effectively discriminate between products' utilities in choice situations. We estimate a discrete choice model of brand choice set formation and preference discrimination on experimental data in two categories—juice and personal computers—and find strong evidence for brand credibility effects and differential mechanisms through which brand credibility's impact materializes on brand choice conditional on choice set, choice set formation, and preference discrimination.
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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 | 0.010 | 0.067 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".