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Record W2005521174 · doi:10.1207/s15327663jcp1504_7

Buyers Versus Sellers: How They Differ in Their Responses to Framed Outcomes

2005· article· en· W2005521174 on OpenAlex
Ashwani Monga, Rui Zhu

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Consumer Psychology · 2005
Typearticle
Languageen
FieldDecision Sciences
TopicDecision-Making and Behavioral Economics
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsDatabase transactionPromotion (chess)Transaction costEconomicsMicroeconomicsMarketingBusinessAdvertisingComputer science

Abstract

fetched live from OpenAlex

Consumers’ reactions to a difference in price can depend on how it is framed. If buyers interpret paying $60 rather than $65 as getting a $5 discount, then they are likely to consider paying $60 to be a gain and paying $65 to be a nongain. Alternatively, if they interpret having to pay $65 rather than $60 as incurring a $5 penalty, then they may consider paying $60 to be a nonloss and paying $65 to be a loss. Similarly, sellers can also experience gains, nongains, nonlosses, and losses. This article suggests that buyers are prevention focused and consequently place a greater emphasis on loss‐related frames, whereas sellers are promotion focused and place a greater emphasis on gain‐related frames. Therefore, for equivalent positive outcomes, buyers feel better about nonlosses, but sellers feel better about gains. For equivalent negative outcomes, buyers feel worse about losses, but sellers feel worse about nongains. These effects, however, disappear when there is little motivation to process information about the monetary transaction.

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 imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.747
Threshold uncertainty score0.667

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.233
GPT teacher head0.481
Teacher spread0.248 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it