Multiple Routes to Self- versus Other-Expression in Consumer Choice
Bibliographic record
Abstract
Studies of consumer decision making often begin with the identification of a dimension on which options differ, followed by an analysis of the factors that influence preferences along that dimension. Building on a conceptual analysis of a diverse set of problems, the authors identify a class of related consumers choices (e.g., extreme vs. compromise, hedonic vs. utilitarian, risky vs. safe) that can all be classified according to their levels of self- versus other-expression (or [un]conventionality). As shown in four studies, these problem types respond similarly to manipulations that trigger or suppress self-expression. Specifically, priming self-expression systematically increases the share of the self-expressive options across choice problems. Conversely, expecting to be evaluated decreases the share of the self-expressive options across the various choice dilemmas. In addition, priming risk seeking increases only the choice of risky gambles but not of other self-expressive options. These findings highlight the importance of seeking underlying shared features across different consumer choice problems, instead of treating each type in isolation.
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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 | 0.014 |
| 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 | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".