Determinants of Digital Payment Adoption Among Generation Z: An Empirical Study
Bibliographic record
Abstract
The main goal of the current paper is to investigate the factors that influence Millennials’ adoption of digital payments among Generation Z by analyzing the potential effects of perceived convenience, perceived cost, perceived security, perceived convenience, innovativeness, and social influence on the adoption of digital payments. A total of 258 individuals in Malaysia were asked to complete a questionnaire to gather statistics. To assess the research model and test the hypotheses, structural equation modeling with partial least squares (SEM-PLS) was utilized. Smart PLS path analysis results revealed that perceived convenience, perceived security, perceived cost social influence, and innovativeness were positively significant determinants of digital payment adoption. This study offers fresh theoretical perspectives for identifying potential adoption barriers that need to be addressed. Concerns about privacy and security, a lack of information or comprehension, and aversion to change are all prevalent challenges among Millennials. Recognizing these limitations allows service providers to incorporate measures such as better security features, educational campaigns, and user-friendly interfaces to alleviate these concerns and boost adoption.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".