The Emergence of E-Wallet in Sarawak: Factors Influencing the Adoption of Sarawak Pay
Bibliographic record
Abstract
This paper discusses the adoption factors of electronic wallet, more specifically Sarawak Pay in Kuching, Sarawak. The Sarawak Government has a very aggressive mandate in terms of digital transformation to boost the e-commerce industry in Sarawak. Propel with the effort of federal and state governments in encouraging a cashless and digital society, Kuching will foresee a rapid rise in the adoption of Sarawak Pay. This paper adopts and expands the Technology Acceptance Model (TAM) in addressing the adoption factors of Sarawak Pay. With a population of three quarter a million, it is vital to understand whether perceived usefulness, perceived ease of use, perceived risk and reward play any significant roles in the adoption of Sarawak Pay. Data were collected from 204 respondents and the results were examined using partial least squares-structured equation modelling (PLS-SEM). Findings indicated that perceived usefulness, perceived ease of use and perceived risk have significant impacts on Sarawak Pay adoption. As a result of the study, the understanding of existing theoretical literature on e-wallets in Malaysia will be enhanced.
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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 | 0.004 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".