Investigating e-wallet adoption of COVID19 intra-period among Malaysian youths': Integrated task-technology fit and technology acceptance model framework
Bibliographic record
Abstract
As embodied in Malaysia's Vision 2020, Malaysia aims to become a cashless country. Therefore, the existing statistical data indicated that the e-wallet adoption rate remains at a low percentage. It has been a barrier for Malaysia in achieving the aims to become a cashless country. The use of e-wallet was also expected to rise amidst the Covid-19 pandemic; to optimize an intervention for the Covid-19 outbreak. Thus, the current study investigates the factors that correlate with the intention to use e-wallet during the Covid-19 pandemic. This study is designed using a quantitative approach through cross-sectional data. A total of 160 Malaysian youths participated and collected by using an online survey. Further, the Task-Technology Fit (TTF) model and Technology Acceptance Model (TAM) were integrated into this study with an extended variable, namely, perceived credibility. The analysis results showed that Individual-Technology Fit, Task-Technology Fit, Perceived Usefulness, Perceived Ease of Use and Perceived Credibility were significantly correlated to Covid19 intra-period e-wallet adoption. In conclusion, a considerable theoretical contribution was demonstrated by integrating TTF-TAM and Perceived Credibility in a single integrated model. The constructs in the TTF model (i.e., Individual-technology fit and task-technology fit) has positively related to the constructs in the TAM model (i.e., perceived usefulness and perceived ease of use). This study is useful to stakeholders and provides enhanced directions to meet market needs by understanding and predicting e-wallet user's post-pandemic behavior, thereby helping service providers attract new users and retain their existing users.
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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 | 0.004 | 0.008 |
| 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 | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".