Impatience and Savoring vs. Dread: Asymmetries in Anticipation Explain Consumer Time Preferences for Positive vs. Negative Events
Bibliographic record
Abstract
For positive experiences (e.g., when to eat a snack), consumers generally prefer to have them immediately, and for negative experiences (e.g., when to pay a bill), consumers often prefer to delay. Yet, across three studies (plus twelve supplemental studies) we find that anticipatory feelings push in the opposite direction, and do so differently for positive vs. negative events, leading to different time preferences: The desire for immediate positives is stronger than the desire to delay negatives. For negative events, anticipatory utility is strongly negative, reducing the desire to delay bad things (i.e., consumers want to “get it over with” to minimize the psychological discomfort), but for positive events, overall anticipatory utility is weakly positive, and therefore does little to reduce consumers’ desire to expedite good things. This anticipatory asymmetry happens because when consumers think about a future positive event, they both enjoy imagining it (savoring) while simultaneously disliking the feeling of waiting for it (impatience), but when consumers think about a negative event, they both dislike imagining it (dread) and dislike the feeling of waiting for it. We demonstrate the managerial implications of these findings in a pair of field studies using online advertisements for retirement planning.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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".