The Influence of Features and Factor on Continuous Usage Intention in Indonesia’s National Health Insurance Application
Bibliographic record
Abstract
The digital transformation of Indonesia's healthcare service system, driven by Social Security Agency on Health (BPJS) initiatives, has made significant strides with the implementation of the National Health Insurance (JKN Mobile) Application. This study, conducted in July 2023, focused on a sample of 119 participants from Indonesia (Jabodetabek area) who were active users of the JKN mobile application and residents of Indonesia. Our. Data was collected using the purposive sampling technique, and the analysis was conducted through Structural Equation Modeling (SEM) and Smart-PLS, a widely recognized statistical tool for hypothesis testing. The results of this study revealed that eight out of ten hypotheses demonstrated a significant impact, shedding light on the factors influencing users' continued intention to utilize the JKN Mobile Application in the Indonesian healthcare landscape. This research contributes valuable insights for enhancing the mobile application's effectiveness and user satisfaction, ultimately contributing to the advancement of telemedicine in Indonesia's healthcare ecosystem.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".