“My life is a mess, I deserve a brownie:” Justifying indulgence by overstating the severity of life problems
Bibliographic record
Abstract
Abstract Consumers frequently experience goal conflict, where they have to choose between staying on course to achieve a goal and succumbing to a tempting indulgence that interrupts goal pursuit. This study introduces a novel strategy consumers use to justify the choice of an indulgent (goal‐conflicting) option over a righteous (goal‐aligned) one. In three experimental studies involving real consumption decisions, the authors show that before choosing a goal‐conflicting option over a goal‐aligned one, consumers overstate the severity of their life problems before making their choice to feel more deserving of the indulgence. This justification strategy is apparent when the goal‐conflicting option is chosen over a goal‐aligned option (vs. over another goal‐conflicting option—i.e., no goal conflict), and when the severity of life problems is reported before (vs. after) making the final choice. Furthermore, the findings reveal a positive downstream consequence of the proposed justification strategy on choice satisfaction. These findings contribute to the growing research on consumers' tendency to create reasons to justify indulgences, in this case at the expense of deliberately degrading one's current state to feel more deserving of indulgence.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.004 | 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".