Zero as a Special Price: The True Value of Free Products
Bibliographic record
Abstract
When faced with a choice of selecting one of several available products (or possibly buying nothing), according to standard theoretical perspectives, people will choose the option with the highest cost–benefit difference. However, we propose that decisions about free (zero price) products differ, in that people do not simply subtract costs from benefits but instead they perceive the benefits associated with free products as higher. We test this proposal by contrasting demand for two products across conditions that maintain the price difference between the goods, but vary the prices such that the cheaper good in the set is priced at either a low positive or zero price. In contrast with a standard cost–benefit perspective, in the zero-price condition, dramatically more participants choose the cheaper option, whereas dramatically fewer participants choose the more expensive option. Thus, people appear to act as if zero pricing of a good not only decreases its cost, but also adds to its benefits. After documenting this basic effect, we propose and test several psychological antecedents of the effect, including social norms, mapping difficulty, and affect. Affect emerges as the most likely account for the effect.
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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 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".