Analysis of influencing factors of long-term care insurance system adoption intention based on UTAUT, technology readiness as the moderator
Bibliographic record
Abstract
This study investigates the determinants of Long-Term Care Insurance (LTCI) adoption intention using the Unified Theory of Acceptance and Use of Technology (UTAUT), with Technology Readiness (TR) as a moderating variable. A quantitative approach was applied, utilizing self-administered questionnaires from 180 participants across health service institutions in Guangxi Province, China. Data were measured on a seven-point Likert scale and analyzed using Structural Equation Modeling (SEM) via SmartPLS. Results confirm that performance expectancy, effort expectancy, social influence, and facilitating conditions significantly influence LTCI adoption intention. Additionally, TR moderates these relationships, strengthening their effects. The findings underscore TR's critical role in enhancing LTCI adoption and offer practical insights for policymakers and practitioners seeking to promote LTCI uptake.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.019 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".