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Record W3012007171 · doi:10.1177/0022243720901520

Mixing It Up: Unsystematic Product Arrangements Promote the Choice of Unfamiliar Products

2020· article· en· W3012007171 on OpenAlexaff
Maik Walter, Christian Hildebrand, Gerald Häubl, Andreas Herrmann

Bibliographic record

VenueJournal of Marketing Research · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicConsumer Behavior in Brand Consumption and Identification
Canadian institutionsUniversity of Alberta
FundersSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
KeywordsProduct (mathematics)PerceptionSet (abstract data type)PreferenceNew product developmentMarketingConsumer choiceChoice setConsumer behaviourField (mathematics)Computer scienceCognitive psychologyBusinessEconomicsMicroeconomicsPsychologyEconometricsMathematics

Abstract

fetched live from OpenAlex

This research examines how the unsystematic (vs. systematic) spatial arrangement of a set of alternatives affects consumers’ product choices. The key hypothesis is that an unsystematic product arrangement—in which an assortment consisting of several alternatives is arranged in an apparently arbitrary manner—causes greater perceptual disfluency, which in turn triggers more extensive exploratory product search, ultimately promoting the choice of unfamiliar products. This sequence of effects is particularly pronounced when consumers do not have a strong prior preference for specific alternatives in the assortment. Evidence from five studies, including a large-scale field experiment, provides support for this theorizing across various display formats and product domains. The findings advance our understanding of how the spatial arrangement of a product assortment influences consumer choice, and they shed light on the psychological mechanism that governs this effect.

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)0.001
metaresearch head score (Gemma)0.004
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 score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
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 head0.187
GPT teacher head0.364
Teacher spread0.177 · 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

Citations22
Published2020
Admission routes1
Has abstractyes

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