Post-adoption model of mobile payment in Indonesia: Integration of UTAUT2 and the dedication-constraint perspective
Bibliographic record
Abstract
Heading towards a cashless society, consumers have undergone a significant shift toward mobile payment services after COVID-19. The proliferation of various mobile payment applications has resulted in low consumer loyalty to mobile payment providers. Thus, continuance intention of mobile payments becomes crucial for mobile payment providers. The integration of UTAUT2 and the dedication-constraint-based mechanism are adopted to elaborate approach in retaining customers. The dedication mechanism is built by examining antecedents of satisfaction. Meanwhile, the constraint mechanism is driven by switching costs, preceded by habit and economic incentives. A total of 297 mobile payment users participated by filling out questionnaires in a field survey. The results show that the dedication mechanism dominates in creating satisfaction by increasing perceived usefulness, while the constraint mechanism is more influenced by habit than economic incentives. This research provides insights for mobile payment providers to enhance satisfaction by understanding consumers' needs in using mobile payments and to increase switching costs by fostering habit, thereby encouraging continuance intention of mobile payments in the future.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".