Integration of technology acceptance model (TAM) and theory of planned behavior (TPB): An e-wallet behavior with fear of covid-19 as a moderator variable
Bibliographic record
Abstract
E-wallet usage in transactions during the Covid-19 pandemic is a cashless movement that supports breaking the chain of transmission of the Covid-19 virus. Intention to use E-wallet during the pandemic is high due to the stay-at-home recommendation that was enforced since the beginning of the breakdown of the first case in Indonesia. Several studies on technology acceptance have been carried out and this study presents a research framework by integrating technology acceptance model (TAM) and theory of planned behavior (TPB) to obtain more comprehensive results to increase technology acceptance intentions by adding virus fear and risk perception to the models that have been tested previously, since the research was conducted when the pandemic is ongoing. The results reveal that perceived usefulness, perceived ease of use, and perceived risk have a direct or indirect effects on attitudes toward using and behavioral intention to use, moderating results of fear of covid-19 on attitudes and intentions as well was found to significantly increase behavioral intention to use E-wallet during the Covid-19 pandemic.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".