The effect of e-WOM through intention to use technology and social media community for mobile payments during the COVID-19
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The use of mobile payments has become a public interest during the current spread of the coronavirus. The use of mobile payments prevents society from touching the payment media. This study examines the effect of ease of use on e-WOM through the intention to use and social media community on the mobile payment method. This research was conducted by taking data through closed questionnaires designed with a five-point Likert scale. This study distributed two hundred fifty questionnaires, and 202 returned to be processed using the partial least square (PLS) technique. The results of data processing show that the ease of use of technology applications had a positive effect on the intention to use an e-WOM. Ease of use of technology has a positive effect on the social media community because of the ease of operation and understanding of the steps for using technology to access and join as members of the social media community. Intention to use in the operation of technology and relatively low cost does not directly affect e-WOM but must go through a community on social media that provides an exciting atmosphere. The social media community has a significant effect on e-WOM. The social media community can share information on social media and share reviews between members so that it creates a sense of trust and mutual concern among members. This study provides an insight into the mobile payment provider to consider the ease of use of their design. This research contributes to the ongoing research in the online payment application study in the pandemic era.
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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 | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 it