The effect of e-WOM through intention to use technology and social media community for mobile payments during the COVID-19
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".