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Record W2153065734 · doi:10.1509/jm.13.0253

Place the Logo High or Low? Using Conceptual Metaphors of Power in Packaging Design

2014· article· en· W2153065734 on OpenAlexaff
Aparna Sundar, Theodore J. Noseworthy

Bibliographic record

VenueJournal of Marketing · 2014
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicConsumer Behavior in Brand Consumption and Identification
Canadian institutionsYork University
Fundersnot available
KeywordsLogo (programming language)Package designAdvertisingFluencyPreferenceMetaphorMarketingBusinessBrand extensionPower (physics)Conceptual modelConceptual frameworkBrand managementComputer sciencePsychologySociologyEngineeringLinguisticsMathematicsEngineering drawingMathematics education

Abstract

fetched live from OpenAlex

Across three studies, this research examines how marketers can capitalize on their brand's standing in the marketplace through strategic logo placement on their packaging. Using a conceptual metaphor framework, the authors find that consumers prefer powerful brands more when the brand logo is featured high rather than low on the brand's packaging, whereas they prefer less powerful brands more when the brand logo is featured low rather than high on the brand's packaging. Furthermore, the authors confirm that the underlying mechanism for this shift in preference is a fluency effect derived from consumers intuitively linking the concept of power with height. Given this finding, the authors then demonstrate an important boundary condition by varying a person's state of power to be at odds with the metaphoric link. The results demonstrate when and how marketers can capitalize on consumers’ latent associations through package design.

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)0.002
metaresearch head score (Gemma)0.005
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.007
Scholarly communication0.0040.006
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.043
GPT teacher head0.256
Teacher spread0.212 · 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

Citations215
Published2014
Admission routes1
Has abstractyes

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