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Record W4391841572 · doi:10.1002/mar.21983

Looking a gift horse in the mouth: Suspicion of large gift expenditures undermines gift appreciation

2024· article· en· W4391841572 on OpenAlexaff
Aybike Mutluoglu, Laurence Ashworth, Nicole Robitaille

Bibliographic record

VenuePsychology and Marketing · 2024
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicConsumer Behavior in Brand Consumption and Identification
Canadian institutionsQueen's University
Fundersnot available
KeywordsGift givingPsychologySocial psychologyHorse racingAdvertisingLawBusinessPolitical science

Abstract

fetched live from OpenAlex

Abstract Prior work shows that gift recipients are surprisingly insensitive to the amount of money givers spend, even though more expensive gifts represent a greater investment by givers and impart greater value to recipients. We suggest that recipients' apparent indifference may be explained by competing reactions to gift expenditure. Specifically, we propose that recipients are not unresponsive to gift expenditure, per se, but that money's association with instrumentality means that conspicuous monetary expenditures can cause recipients to contemplate givers' instrumental motives (i.e., become suspicious). Four studies show that large gift expenditures can cause recipients to become suspicious of givers' motives and that suspicion undermines recipients' otherwise positive reactions. We further show that expenditures that are less strongly associated with instrumentality (time and effort) and gifts that have a weaker association with money and instrumentality (experiential gifts) are less prone to suspicion and are appreciated more.

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)0.005
metaresearch head score (Gemma)0.030
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 score0.005
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.030
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.025
GPT teacher head0.316
Teacher spread0.292 · 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

Citations4
Published2024
Admission routes1
Has abstractyes

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