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Record W4280588599 · doi:10.18280/isi.270206

Factors Influencing Adoption of Mobile Health Monitoring System: Extending UTAUT2 with Trust

2022· article· en· W4280588599 on OpenAlexvenueno aff
Noor Dheyaa Azeez, Niebal Younis Mohammed

Bibliographic record

VenueIngénierie des systèmes d information · 2022
Typearticle
Languageen
FieldDecision Sciences
TopicTechnology Adoption and User Behaviour
Canadian institutionsnot available
Fundersnot available
KeywordsUnified theory of acceptance and use of technologyPerspective (graphical)Affect (linguistics)Value (mathematics)Knowledge managementBusinessPsychologyComputer scienceSocial influenceSocial psychology

Abstract

fetched live from OpenAlex

Mobile patient monitoring systems are among the most important systems that seek to enhance the efficiency and effectiveness of services provided to patients. The study would aim to investigate the influencing factors of ambulatory patient monitoring system adoption from the patients' perspective. The proposed model in this study included the (UTAUT2) factors of the extended unified theory of technology acceptance with the addition of a confidence factor. To collect the data, a questionnaire was used, which was distributed to 200 participants. The results of the study showed that performance expectations, price value, effort expectation, and confidence positively and significantly affect behavioral intention. Moreover, in the last section, the study's directions and future directions will be discussed.

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 score0.371
Threshold uncertainty score0.725

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.002
Open science0.0010.000
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 head0.066
GPT teacher head0.328
Teacher spread0.262 · 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

Citations11
Published2022
Admission routes1
Has abstractyes

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