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Record W2168315542 · doi:10.1089/cyber.2014.0675

Can Coolness Predict Technology Adoption? Effects of Perceived Coolness on User Acceptance of Smartphones with Curved Screens

2015· article· en· W2168315542 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueCyberpsychology Behavior and Social Networking · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicDigital Marketing and Social Media
Canadian institutionsKootenay Association for Science & Technology
FundersNational Research Foundation of Korea
KeywordsAttractivenessUsabilityTechnology acceptance modelStructural equation modelingOriginalityPsychologyComputer scienceAestheticsHuman–computer interactionAdvertisingSocial psychologyBusinessArtMachine learningCreativity

Abstract

fetched live from OpenAlex

This study proposes an acceptance model for curved-screen smartphones, and explores how the sense of coolness induced by attractiveness, originality, subcultural appeal, and the utility of the curved screen promotes smartphone adoption. The results of structural equation modeling analyses (N = 246) show that these components of coolness (except utility) increase the acceptance of the technology by enhancing the smartphones' affectively driven qualities rather than their utilitarian ones. The proposed coolness model is then compared with the original technology acceptance model to validate that the coolness factors are indeed equally effective determinants of usage intention, as are the extensively studied usability factors such as perceived ease of use and usefulness.

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.

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)0.001
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 score0.192
Threshold uncertainty score0.819

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.002
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 head0.024
GPT teacher head0.301
Teacher spread0.277 · 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