Service Provider Salience: When Guilt Undermines Consumer Willingness to Buy Time
Bibliographic record
Abstract
Spending money on time-saving services can improve happiness and reduce stress. Yet many people do not spend money to save time even when they can afford to do so, potentially because they feel guilty about paying other people to complete disliked tasks on their behalf. Consistent with this proposition, we find evidence that individuals are most likely to experience guilt when outsourcing to a salient service provider. Across two large-scale surveys of working adults, including a nationally representative sample of employed Americans (Study 1a & 1b, N = 1,337), individuals reported greater guilt when they thought about outsourcing to a salient (vs. non-salient) service provider. Using a novel lab paradigm, participants felt greater guilt when the service provider was salient, which in turn undermined their willingness to buy time (Study 2, N = 350). In Study 3, these effects were mitigated by emphasizing the benefits of task completion for the service provider (N = 390). This research points to the potential of simple interventions to help organizations encourage individuals to make time-saving purchases.
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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 | 0.007 |
| 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 | 0.001 | 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".