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Record W3197482898 · doi:10.5267/j.ijdns.2021.8.002

Integration of technology acceptance model and theory of reasoned action in pre-dicting e-wallet continuous usage intentions

2021· article· en· W3197482898 on OpenAlexvenueno aff
Putu Laksmita Dewi Rahmayanti, I Gusti Ngurah Jaya Agung Widagda, Ni Nyoman Kerti Yasa, I Gusti Ayu Ketut Giantari, Martaleni Martaleni, Dwi Putra Buana Sakti, Suwitho Suwitho, Putri Anggreni

Bibliographic record

VenueInternational Journal of Data and Network Science · 2021
Typearticle
Languageen
FieldDecision Sciences
TopicTechnology Adoption and User Behaviour
Canadian institutionsnot available
Fundersnot available
KeywordsTheory of reasoned actionTechnology acceptance modelUsabilityStructural equation modelingAction (physics)Theory of planned behaviorPsychologyComputer scienceSocial psychologyControl (management)Human–computer interactionArtificial intelligence

Abstract

fetched live from OpenAlex

The purpose of this study is to determine the factors influencing continuous usage intention of E-Wallet in Denpasar City with integrating the Technology Acceptance Model (TAM) And Theory of Reasoned Action (TRA). This study applied a structural equation model analysis with 140 samples collected from E-Wallet users in Denpasar City. The results show that all three determinants of E-Wallet continuous usage intention, including perceived usefulness, perceived ease of use, and attitude. Recommendations are provided for E-Wallet providers to improve their user continuous usage intention in Denpasar City.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.005
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.013
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0020.003
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.136
GPT teacher head0.425
Teacher spread0.289 · 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 source (direct Gemma or distilled Codex), 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

Citations24
Published2021
Admission routes1
Has abstractyes

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Same venueInternational Journal of Data and Network ScienceSame topicTechnology Adoption and User BehaviourFrench-language works237,207