True Context‐dependent Preferences? The Causes of Market‐dependent Valuations
Bibliographic record
Abstract
ABSTRACT A central assumption of neoclassical economics is that reservation prices for familiar products express people's true preferences for these products; that is, they represent the total benefit that a good confers to the consumers and are, thus, independent of actual prices in the market. Nevertheless, a vast amount of research has shown that valuations can be sensitive to other salient prices, particularly when individuals are explicitly anchored on them. In this paper, the authors extend previous research on single‐price anchoring and study the sensitivity of valuations to the distribution of prices found for a product in the market. In addition, they examine its possible causes. They find that market‐dependent valuations cannot be fully explained by rational inferences consumers draw about a product's value and are unlikely to be fully explained by true market‐dependent preferences. Rather, the market dependence of valuations likely reflects consumers' focus on something other than the total benefit that the product confers to them. Furthermore, this paper shows that market‐dependent valuations persist when – as in many real‐life settings – individuals make repeated purchase decisions over time and infer the distribution of the product's prices from their market experience. Finally, the authors consider the implications of their findings for marketers and consumers. Copyright © 2013 John Wiley & Sons, Ltd.
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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 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".