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Record W4293214969 · doi:10.5267/j.ijdns.2022.7.008

Attitude based on Tri Kaya Parisudha in increasing intention to reuse e-money

2022· article· en· W4293214969 on OpenAlexvenueno aff
Nyoman Dwika Ayu Amrita, Wayan Gede Supartha, I Gusti Ayu Ketut Giantari, Ni Wayan Ekawati

Bibliographic record

VenueInternational Journal of Data and Network Science · 2022
Typearticle
Languageen
FieldHealth Professions
TopicHealth, Technology, Consumer Behavior
Canadian institutionsnot available
Fundersnot available
KeywordsRisk perceptionReuseNonprobability samplingNorm (philosophy)PerceptionSocial psychologyPsychologySample (material)MedicineEngineeringPolitical scienceEnvironmental healthLawPopulation

Abstract

fetched live from OpenAlex

The purpose of the study is to explain the effect of perceived risk and subjective norm to the attitude based on Tri Kaya Parisudha and reuse intentions, attitude to reuse, and role attitude in mediating the effect on perceived risk and norm subjective to reuse intention from the use of e-money in the elderly. Quantitative approach is conducted for this study. The subject is the elderly/senior citizen who uses e-money, with a sample of 150 respondents. Purposive sampling is used as a method of sampling determination with the SEM-PLS technique. The result of this study are attitude based on Tri Kaya Parisudha capable mediate influence perception risk and norm subjective to reuse intention, also this study found the attitudes based on Tri Kaya Parisudha turned out to be able to mediate the effect of risk perception and also the influence of subjective norms on the intention to reuse e-money in the elderly in the city of Denpasar and the elderly who, even though they are elderly, still use e-money to meet their needs.

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)0.011
metaresearch head score (Gemma)0.002
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 score0.060
Threshold uncertainty score0.489

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0030.002
Research integrity0.0000.001
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 head0.126
GPT teacher head0.489
Teacher spread0.363 · 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

Citations4
Published2022
Admission routes1
Has abstractyes

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