Reversing the endowment effect by empowering buyers and sellers
Bibliographic record
Abstract
Purpose The endowment effect is well-established in economics, psychology and marketing where sellers place a higher value on a good than buyers. One potential moderator, namely, power is explored. The authors predicted that feeling powerful can reverse the effect, making buyers place a higher value on a good than sellers. Design/methodology/approach The authors manipulated power to assess the effects on the valuation of three different products (keychain, gift card and iPhone case). They also assessed participants’ focus on parting with the good (money), which is a loss, and receiving money (the good), which is a gain, for sellers (buyers). Findings Feelings of power reduced sellers’ prices but they increased buyers’. Crucially, the authors observed the endowment effect, but only under conditions of low power. When participants had high power, the effect reversed, with buyers placing a higher value on the good under transaction than sellers. Process data indicated that powerful buyers and sellers focused on what they gained and less on what they lost, compared to powerless buyers and sellers. Research limitations/implications The authors link the construct of power with the endowment effect, showing that the former can moderate the latter. Certainly, the endowment effect is well-established, but there are moderators and boundary conditions that warrant consideration. Practical implications The results suggest a case where the market may clear, where buyers value a consumer product more than sellers, and thus buyers would likely accept the offer made by sellers. Originality/value The authors are the first to link the power literature with the endowment effect. They also show a possible moderator for the well-established endowment effect.
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.
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 | 0.007 | 0.030 |
| 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 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 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".