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Record W3124254735 · doi:10.1509/jmr.09.0421

When Guilt Begets Pleasure: The Positive Effect of a Negative Emotion

2012· article· en· W3124254735 on OpenAlex

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 Marketing Research · 2012
Typearticle
Languageen
FieldPsychology
TopicEmotions and Moral Behavior
Canadian institutionsKellogg's (Canada)
Fundersnot available
KeywordsPleasureValence (chemistry)PsychologySocial psychologyAffect (linguistics)Consumption (sociology)CognitionContrast (vision)Negative emotionAssociation (psychology)HedonismCognitive psychologyAestheticsPsychotherapistChemistryCommunicationPhilosophy

Abstract

fetched live from OpenAlex

Understanding how emotions can affect pleasure has important implications both for people and for firms’ communication strategies. Prior research has shown that experienced pleasure often assimilates to the valence of one's active emotions, such that negative emotions decrease pleasure. In contrast, the authors demonstrate that the activation of guilt, a negative emotion, enhances the pleasure experienced from hedonic consumption. The authors show that this effect occurs because of a cognitive association between guilt and pleasure, such that activating guilt can automatically activate cognitions related to pleasure. Furthermore, the authors show that this pattern of results is unique to guilt and cannot be explained by a contrast effect that generalizes to other negative emotions. The article concludes with a discussion of the implications of these findings for marketing and consumption behavior.

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)0.039
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.345
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0390.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.087
GPT teacher head0.437
Teacher spread0.350 · 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