MétaCan
Menu
Back to cohort
Record W2090441642 · doi:10.4236/ti.2013.42013

Research on User Experience Quality Assessment Model of Smart Mobile Phone

2013· article· en· W2090441642 on OpenAlexvenueno aff
Jiangping Wan, Yahui Zhu, Jiajun Hou

Bibliographic record

VenueTechnology and Investment · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicDigital Marketing and Social Media
Canadian institutionsnot available
FundersNational Natural Science Foundation of ChinaNational Science Foundation
KeywordsUsabilityMobile phoneComputer scienceSmart phoneUser experience designQuality (philosophy)EntertainmentHuman–computer interactionAnalytic hierarchy processMultimediaEngineeringTelecommunicationsOperations research

Abstract

fetched live from OpenAlex

The user experience influencing factors of smart mobile phone is explored in order to assess its quality. At first, we discover user experience influencing factors of smart mobile phone and establish user experience quality assessment model with grounded theory, which include both environmental experience and user experience, then calculate the weight for each factor with analytic hierarchy process: interaction (0.115), usability (0.283), durability (0.091), innovation (0.104), screen vision (0.098), appearance design (0.071), touch experience (0.057), entertainment(0.133), emotional beggar (0.048), and first four key influencing factors are discovered as follows: usability, entertainment, interaction and innovation. Finally, the model is verified through quality assessment for five smart mobile phones, we hope that it can give some inspiration to mobile phone manufacturers and operators.

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)0.001
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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.174
Threshold uncertainty score0.658

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.115
GPT teacher head0.435
Teacher spread0.320 · 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 designTheoretical or conceptual
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

Citations4
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueTechnology and InvestmentSame topicDigital Marketing and Social MediaFrench-language works237,207