E-WALLET ADOPTION: A CASE IN MALAYSIA
Bibliographic record
Abstract
In line with the rapid growth in internet access, Fintech, online shopping and cross border trading in recent years, mobile payment transactions are expected to be the most prevalent means to complete sales transactions. A RM30 incentive of the use of E-wallet was announced for the Malaysia Budget 2020 to spur the use of E-wallet in Malaysia, while the central bank of Malaysia (BNM) has launched the Financial Sector Blueprint 2011-2020 aiming to eliminate the issuance of cheques and to increase e-payments, which accelerate the speed of transformation into a cashless society and stimulate the shift towards the electronic payment era. This paper contributes by examining the E-wallet adoption behavior of Malaysian smartphone users. The UTAUT model has been used. Data from 210 respondents were collected through an online survey. The findings show that three quarter of Malaysians have tried or started to use E-wallet, despite that it is still not a very common payment option. Half are spending less than RM100 per month using E-wallet with the average amount per transaction of not more than RM50. Partial-least-squares-structural-equation-modelling (PLS-SEM) is applied. The results reveal performance expectation, effort expectation and social influence have positive impact on the use behavior of E-wallet, whilst the perceived risk and perceived costs have no significant influence. Being at the infant stage of E-wallet in Malaysia, the regulators and retailers should focus their efforts on promoting the benefits brought by
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".