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Record W2519708027 · doi:10.1509/jmr.14.0125

Rejecting Responsibility: Low Physical Involvement in Obtaining Food Promotes Unhealthy Eating

2016· article· en· W2519708027 on OpenAlexaff
Linda Hagen, Aradhna Krishna, Brent McFerran

Bibliographic record

VenueJournal of Marketing Research · 2016
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicConsumer Behavior in Brand Consumption and Identification
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsAttributionFood choiceHealthy eatingPsychologyHealthy foodConsumption (sociology)Unhealthy foodSocial psychologyPhysical activityAdvertisingMarketingFood scienceMedicineBusinessObesity

Abstract

fetched live from OpenAlex

Five experiments show that less physical involvement in obtaining food leads to less healthy food choices. The authors find that when participants are given the choice of whether to consume snacks that they perceive as relatively unhealthy, they have a greater inclination to consume them when less (vs. more) physical involvement is required to help themselves to the food; this is not the case for snacks that they perceive as relatively healthy. Further, when participants are given the opportunity to choose their portion size, they select larger portions of unhealthy foods when less (vs. more) physical involvement is required to help themselves to the food; again, this is not the case for healthy foods. The authors suggest that this behavior occurs because being less physically involved in serving one's food allows participants to reject responsibility for unhealthy eating and thus to feel better about themselves after indulgent consumption. These findings add to the research on consumers’ self-serving attributions and to the growing literature on factors that nudge consumers toward healthier eating decisions.

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)0.003
metaresearch head score (Gemma)0.007
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 score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.103
GPT teacher head0.365
Teacher spread0.262 · 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

Citations54
Published2016
Admission routes1
Has abstractyes

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