Spending Windfall (“Found”) Time on Hedonic versus Utilitarian Activities
Bibliographic record
Abstract
Abstract Consumers often gain extra free time unexpectedly. Given the increasing time pressure that consumers experience in their daily lives, it is important to understand how they spend windfall (or unexpected) free time, which we term found time. In a series of five laboratory experiments and naturalistic field studies, we found that consumers spend more of their free time on hedonic activities than on utilitarian activities when they gain the time unexpectedly (i.e., found time), but not when they know about the free time in advance. This pattern occurs consistently regardless of whether consumers gain the time from canceled work-related or leisure activities. In addition, our studies uncovered perceived busyness as a ubiquitous yet unexplored moderator for the windfall gain literature: the inclination to allocate found time to hedonic consumption decreases when consumers perceive themselves to be busy at the moment. We discuss several potential accounts for the effect of unexpectedness on time expenditure, including a perceived fit between the nature of found time (a fun windfall gain) and hedonic consumption, need for justification, and 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 | 0.001 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".