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Record W2791804351

SUBJECTIVE NORMS, SELF-EFFICACY AND GOVERNMENT SUPPORT TO INTENTION TO USE INTERNET BANKING (IN ISLAMIC PERSPECTIVE)

2018· article· en· W2791804351 on OpenAlexvenueno aff
Kirana Kc, Ratnasari Rt, Tika Widiastuti

Bibliographic record

VenueThe Journal of Internet Banking and Commerce · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicIslamic Finance and Communication
Canadian institutionsnot available
Fundersnot available
KeywordsThe InternetStructural equation modelingGovernment (linguistics)Test (biology)Perspective (graphical)PsychologyIslamStatisticValidityApplied psychologySocial psychologyComputer sciencePsychometricsClinical psychologyStatisticsWorld Wide WebMathematicsArtificial intelligence
DOInot available

Abstract

fetched live from OpenAlex

The subjects of this study were santri who use internet banking in Yogyakarta. The study use questionnaire survey method. Its exogenous variables were Subjective Norms, Self-Efficacy and Government Support, while its endogen variable is Intention Internet Banking. Data were analysis using multi variant structural Equation Modeling. Software used in processing data was AMOS and SPSS. The test results used were validity test, and reliability test. The objective of this study is to understand the influence of Subjective norms, Self-Efficacy and Government Support to Intention Internet Banking Santri in Yogyakarta in Islamic perspective. The study was conducted by distributing 400 questionnaires to santri who use internet banking started from December 2015 to April 2016. Qualified questionnaires were 341 (85, 24%). Based on sex male (santri) were 68, 12 % and female (Santriwati) were 31, 87 %. Base on the research can be concluded that subjective norms are influence to intention internet banking (1,257) but statistic result show not significant. And self-Efficacy is positive significant influence to intention internet banking (1,477). And Government Support is positive significant influence to intention internet banking (0,107).

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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.815
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.021
GPT teacher head0.296
Teacher spread0.274 · 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 designQualitative
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

Citations2
Published2018
Admission routes1
Has abstractyes

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