Collaboration of digital payment usage decision in COVID-19 pandemic situation: Evidence from Indonesia
Bibliographic record
Abstract
This study aims to provide an attempt by raising a framework for assessing the digital technology perspective in the application of Financial Technology by consumers, especially in the era of the Covid19 pandemic in 2020 in Indonesia. Digital technology in Fintech in collaboration with online transportation is utilized by quite a few big firms in Indonesia to meet the needs of consumers during strict, large-scale restrictions but not lockdown. This paper mainly acknowledged the problem related to digitizing solid digital technology which prioritizes technology 4.0. Digital technology applications, especially among the millennial generation regarding the accessibility, pace and value of financial services are increasingly in demand. This research spent 5.5 months with millennial respondents who are accustomed to using everyday technology applications in Jakarta, Depok and Tangerang and surrounding areas. The method of analyzing data in a quantitative way to find findings is complemented by discussion. The findings prove that; All variables have positive strong effect on driving the choice of digital FinTech technology in ordering food and others to survive during the pandemic of COVID-19. The existence of digital-based technology applications related to the internet, big data, smart mobile phones, safe and comfortable technology power has motivated consumers to use them. In conclusion, there are several new business opportunities open to newcomers in the digital financial sector and other accessories using information systems and information ecosystems.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".