Analisis Intensi Perilaku Untuk Mengadopsi dan Merekomendasikan Aplikasi Mobile Payment dengan Metode Structural Equation Modelling
Bibliographic record
Abstract
Indonesian people should turn to mobile payments, which will have an impact on economic efficiency because physical money is inefficient in use today. This study aims to identify the structural effects of Diffusion of Technology (DOI), Extended Unified Theory of Acceptance 2 (UTAUT2), and Perceieved Technology Security (PTS) models on behaviour intention to adopt (BIA) to adopt e-wallet-based mobile payment applications at Go-Pay and OVO using Structural Equation Modelling (SEM) Method. The second objective is to make structural identification of the BIA influence on behaviour intention to recommend (BIR) for the use of e-wallet-based mobile payment applications at Go-Pay and OVO. The results achieved in this study is that the latent compatibility variable significantly influences the behavior intention to adopt (BIA) with a coefficient value of 0.330, whereas innovativeness and perceived technology security have a positive but not significant effect on BIA variables. The variable intention to recommend (BIR) is significantly and positively influenced by the latent BIA variable and has an effect of 0.880.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".