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Record W2423231142 · doi:10.1002/hfm.20679

The Impact of Image Dimensions toward Online Consumers’ Perceptions of Product Aesthetics

2016· article· en· W2423231142 on OpenAlexaff
Jungkun Park, Frances Gunn

Bibliographic record

VenueHuman Factors and Ergonomics in Manufacturing & Service Industries · 2016
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicConsumer Behavior in Brand Consumption and Identification
Canadian institutionsToronto Metropolitan University
FundersHanyang University
KeywordsPerceptionProduct (mathematics)PremiseStructural equation modelingValue (mathematics)MarketingPsychologyUniquenessAdvertisingSample (material)Consumer behaviourBusinessSocial psychologyComputer scienceMathematicsEpistemology

Abstract

fetched live from OpenAlex

ABSTRACT Marketers and industrial designers devote considerable attention to the visual attributes of products, based on the premise that the visual appearance of products influences consumers’ judgments of the products’ attributes. This research investigated consumers’ perceptions about particular types of innovative products (revolutionary technology‐driven products), with 275 consumers sample purchased from an independent marketing company. To achieve the main goal, interrelations among image of product and aesthetics of product have been examined using structural equation modeling with two psychological moderators: consumer innovativeness and needs for uniqueness. The results of this study provide evidence that individual differences in uniqueness motivation moderated how online consumers’ perceptions of a product's image characteristics influenced perceptions of value showing consumers’ need for uniqueness was more influential toward perceptions of the product's value than perceptions of functional value. Consequently, these findings expand understanding of the consumer characteristics that respond to perceptions of products’ epistemic value.

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)0.000
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 score0.051
Threshold uncertainty score0.516

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.048
GPT teacher head0.283
Teacher spread0.235 · 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

Citations7
Published2016
Admission routes1
Has abstractyes

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