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Record W4377989449 · doi:10.1002/mar.21834

The effect of perceived control on local consumption

2023· article· en· W4377989449 on OpenAlexafffund
Arani Roy, Ashesh Mukherjee

Bibliographic record

VenuePsychology and Marketing · 2023
Typearticle
Languageen
FieldPsychology
TopicBehavioral Health and Interventions
Canadian institutionsMcGill University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsModerationPsychologyConsumption (sociology)Social psychologyIncentiveConsumer behaviourFeelingPerceived controlControl (management)Antecedent (behavioral psychology)Quality (philosophy)Robustness (evolution)MarketingEconomicsMicroeconomicsBusinessSociology

Abstract

fetched live from OpenAlex

Abstract Local consumption improves the economic health of local communities and reduces the environmental impact of marketing activities, and hence it is important to understand factors that increase the likelihood of local consumption. Across eight studies in the laboratory and field, we show that the likelihood of local consumption increases as consumers' perceived control decreases with this effect being mediated by feelings of anticipated warm glow. We also identify two boundary conditions of this effect, namely perceived quality of the product and public self‐consciousness of the consumer. This research contributes to the literature in the following ways. First, it identifies perceived control as a novel self‐discrepancy‐based antecedent of the likelihood of local consumption. Second, it identifies anticipated warm glow as a novel affective mechanism underlying the effect of perceived control on the likelihood of local consumption. Third, it identifies perceived quality of the local product as a novel moderator of the effect of perceived control on the likelihood of local consumption. Fourth, it identifies public self‐consciousness of the consumer as another novel moderator of the effect of perceived control on the likelihood of local consumption. This research also contributes methodologically by demonstrating robustness of effects across a range of manipulations and measures including incentive‐compatible behavior and online 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.

How this classification was reachedexpand

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.729
Threshold uncertainty score0.411

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.036
GPT teacher head0.417
Teacher spread0.381 · 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 teacher head, 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

Citations10
Published2023
Admission routes2
Has abstractyes

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