MétaCan
Menu
Back to cohort
Record W4327723573 · doi:10.1007/s11747-023-00928-4

Feeling the values: How pride and awe differentially enhance consumers’ sustainable behavioral intentions

2023· article· en· W4327723573 on OpenAlexaff
Yan Li, Hean Tat Keh, Kyle B. Murray

Bibliographic record

VenueJournal of the Academy of Marketing Science · 2023
Typearticle
Languageen
FieldArts and Humanities
TopicMedia Influence and Health
Canadian institutionsUniversity of Alberta
FundersNational Natural Science Foundation of ChinaMonash University
KeywordsPrideValue (mathematics)FeelingConsumption (sociology)PsychologyPreferenceSocial psychologyConsumer behaviourMarketingBusinessSociologyEconomicsPolitical scienceMicroeconomicsComputer science

Abstract

fetched live from OpenAlex

Abstract Building on prior work examining discrete emotions and consumer behavior, the present research proposes that consumers are more likely to engage in the target sustainable behavior when marketers use an emotional appeal that matches the brand’s expressed values or one that is congruent with consumers’ value priority. In particular, we focus on two contrasting positive emotions—pride and awe. We show that the effectiveness of pride and awe appeals depends on the corresponding human values. Specifically, pride increases sustainable behavior and intentions when the self-enhancement value is prioritized; and awe increases sustainable behavior and intentions when the self-transcendence value is prioritized. Importantly, this interaction can be explained by enhanced self-efficacy. We demonstrate these effects across six studies, including a field study. Our findings contribute to a better understanding of sustainable consumption, reconcile prior research, and provide practical guidance for marketers and policy-makers.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.054
GPT teacher head0.327
Teacher spread0.273 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations86
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueJournal of the Academy of Marketing ScienceSame topicMedia Influence and HealthFrench-language works237,207