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Record W2064661564 · doi:10.1002/mar.20162

Developing heuristic‐based quality judgments: Blocking in consumer choice

2007· article· en· W2064661564 on OpenAlexaff
Robert J. Oxoby, Hugh Finnigan

Bibliographic record

VenuePsychology and Marketing · 2007
Typearticle
Languageen
FieldDecision Sciences
TopicDecision-Making and Behavioral Economics
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsHeuristicsQuality (philosophy)Blocking (statistics)Affect (linguistics)Product (mathematics)Order (exchange)HeuristicPsychologyProcess (computing)AdvertisingBlock (permutation group theory)MarketingInformation qualityComputer scienceBusinessInformation systemArtificial intelligenceCommunication

Abstract

fetched live from OpenAlex

Abstract This article provides an experimental demonstration of how the sequential order in which consumers receive information can influence the way information is processed and affect consumers' decisions. Specifically, when participants initially receive information regarding brand/quality or price/quality associations, these associations can block consumers' attention to more relevant quality‐determining physical attributes. Moreover, this process of blocking can carry over to affect quality judgments pertaining to similarly branded or priced products beyond the product in which blocking was initiated. This implies that consumers' judgments of quality may be heavily dependent on first impressions that develop into brand and price heuristics. © 2007 Wiley Periodicals, Inc.

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)0.024
metaresearch head score (Gemma)0.010
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.554
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0240.010
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 head0.268
GPT teacher head0.523
Teacher spread0.254 · 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.

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

Citations27
Published2007
Admission routes1
Has abstractyes

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