Determining the intention to use app-based medicine service in an emerging economy
Bibliographic record
Abstract
The study investigates the customers’ intention to use app-based medicine services in an emerging economy. This study explores the indirect effects of perceived usefulness, perceived ease of use, perceived security and perceived delivery with the intention to use app-based medicine services through the mediating effect of perceived trust. The present study developed a self-administered survey questionnaire to collect data from 336 respondents who were using app-based medicine services in Bangladesh. The data was collected between March 2022 and May 2022. The collected data were analysed using SmartPLS-4 to determine the reliability and validity of the constructs. The study's findings indicate that perceived usefulness, perceived ease of use, perceived security, and perceived delivery positively and significantly (t > 1.96; P < 0.05) influence the perceived trust in app-based medicine services. The research findings also indicate that perceived ease of use, perceived delivery, and perceived trust significantly (t > 1.96; P < 0.05) impact the intention to use app-based medicine services. This study highlights to explore the success factors such as consumer perceived usefulness, perceived ease of use, perceived security, and perceived delivery that can increase customers’ trust to use app-based medicine services in the developing economy.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".