MétaCan
Menu
Back to cohort
Record W2170574464 · doi:10.1509/jmr.10.0384

Looks Interesting, but what does it Do? Evaluation of Incongruent Product form Depends on Positioning

2011· article· en· W2170574464 on OpenAlexaff
Theodore J. Noseworthy, Remi Trudel

Bibliographic record

VenueJournal of Marketing Research · 2011
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicConsumer Behavior in Brand Consumption and Identification
Canadian institutionsWestern University
Fundersnot available
KeywordsProduct (mathematics)MediationConsumption (sociology)MarketingPosition (finance)Product categoryBusinessPsychologyAdvertisingSociologyMathematics

Abstract

fetched live from OpenAlex

Marketers struggle with how best to position innovative products that are incongruent with consumer expectations. Compounding the issue, many incongruent products are the result of innovative changes in product form intended to increase hedonic appeal. Crossing various product categories with various positioning tactics in a single meta-analytic framework, the authors find that positioning plays an important role in how consumers evaluate incongruent form. The results demonstrate that when a product is positioned on functional dimensions, consumers show more preferential evaluations for moderately incongruent form than for congruent form. However, when a product is positioned on experiential dimensions, consumers show more preferential evaluations for congruent form than for moderately incongruent form. Importantly, an increase in perceived hedonic benefits mediates the former, whereas a decrease in perceived utilitarian benefits mediates the latter. The mediation effects are consistent with the view that consumers must first understand a product's functionality before engaging in hedonic consumption.

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)0.016
metaresearch head score (Gemma)0.047
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 score0.016
Threshold uncertainty score0.083

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.047
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0000.002
Scholarly communication0.0040.003
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.211
GPT teacher head0.383
Teacher spread0.172 · 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

Citations127
Published2011
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Marketing ResearchSame topicConsumer Behavior in Brand Consumption and IdentificationFrench-language works237,207