When waiting pays off: The impact of delayed goal failure on self‐perception and self‐regulation
Bibliographic record
Abstract
Abstract Consumers are often exposed to recurrent temptations that might threaten the achievement of their long‐term goals (e.g., savings, diet), and while they might initially resist the temptation, they may find that with the passage of time, they eventually indulge in the goal‐conflicting act. In such instances, does the ultimate goal failure undermine consumers' perceptions of self‐control, or does the mere act of delaying the goal transgression serve to buffer against negative self‐views? In the current research, we term delayed goal failure the sequence of events whereby a consumer initially resists a goal‐conflicting temptation, but upon subsequent exposure to the same temptation, follows through with the goal transgression. Our findings show that delayed (vs. immediate) goal failure allows consumers to maintain positive perceptions of self‐control, particularly when the cause of failure is unspecified (i.e., open to interpretation), allowing consumers to interpret their ultimate decision as thought‐through and justified. Finally, our findings reveal a positive downstream effect of delayed (vs. immediate) goal failure on subsequent self‐regulation, and identify positive perceptions of self‐control as the underlying driver of this effect.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.002 |
| 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".