Production Enjoyment Asymmetrically Impacts Buyers’ Willingness to Pay and Sellers’ Willingness to Charge
Bibliographic record
Abstract
With the rise of social media and the peer-to-peer economy, sellers can easily tell potential buyers about themselves and their process of producing products and services. This research investigates the influence of a central aspect of the production process that sellers can communicate—their production enjoyment. Buyers are willing to pay a higher price, are more likely to click on ads, and are more likely to choose a product or service when the seller signals that they enjoy producing it. In contrast, sellers are willing to accept lower prices, and actually charge less, for products and services they enjoy producing. Both buyers and sellers make the inference that production enjoyment leads to higher quality products/services, but only buyers rely on this inference when forming their pricing judgments relative to sellers. Nine studies illustrate these effects across a wide variety of products and services, participant samples, and operationalizations of production enjoyment. They show that signals of production enjoyment can influence buyers more than other established signals (e.g., effort) and demonstrate contexts where these effects are more and less likely to occur. These findings offer practical recommendations for both buyers and sellers as well as a variety of theoretical contributions.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 0.001 |
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".