Production Enjoyment Asymmetrically Impacts Buyers’ Willingness to Pay and Sellers’ Willingness to Charge
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it