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Record W1582663598 · doi:10.1002/mar.20764

Understanding the Role of Consumer Heterogeneity in the Formation of Satisfaction Uncertainty

2014· article· en· W1582663598 on OpenAlexaff
Cheng Qian, Murali Chandrashekaran, Kangkang Yu

Bibliographic record

VenuePsychology and Marketing · 2014
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicConsumer Behavior in Brand Consumption and Identification
Canadian institutionsUniversity of British Columbia
FundersNational Natural Science Foundation of China
KeywordsAffect (linguistics)PsychologyPleasureSocial psychologyCognitionRegulatory focus theoryEmpirical researchFocus (optics)Closure (psychology)ArousalCognitive appraisalNeed for cognitionCognitive psychologyEconomicsStatistics

Abstract

fetched live from OpenAlex

ABSTRACT Prior empirical research has focused on the antecedents and consequences of attitude uncertainty. Drawing on regulatory focus theory and need for closure theory, this research examines the role of individual difference variables in shaping satisfaction uncertainty. This empirical work seeks to explore the interplay of individual difference variables, cognition and affect, in shaping satisfaction uncertainty. The proposed model maintains that need for closure and regulatory focus shape satisfaction uncertainty through their influence on cognitive and affective processes. The model was tested on 192 participants in an experiment using a restaurant scenario. Satisfaction uncertainty is estimated, rather than measured, using the Judgment Uncertainty and Magnitude Parameters (JUMP) model. The results show that prior expectation, pleasure, and arousal have positive effects on satisfaction uncertainty, while perceived performance has a negative impact. Furthermore, regulatory focus is found to moderate the effects of cognition and affect on satisfaction uncertainty, while need for closure moderates the impact of affect on satisfaction uncertainty.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.316
Threshold uncertainty score0.138

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 head0.060
GPT teacher head0.290
Teacher spread0.230 · 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
Published2014
Admission routes1
Has abstractyes

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