Determinants of Behavioral Intention to Use Digital Payment among Indian Youngsters
Bibliographic record
Abstract
In the current study, we sought to construct an integrated model to identify various elements and evaluate the impact of these identified factors on customers’ behavioral intention to use or not use specific M-wallets for payment. To this end, we proposed and validated a conceptual model. In all, 600 questionnaires were distributed, and 482 responses were deemed usable. Structural equation modeling was used to demonstrate the stability of the proposed model and to test the research hypotheses. Perceived value, trust, compatibility, and social influence were all found to have a substantial influence on behavioral intention; however, consumers are less likely to use an M-wallet on the basis of perceived enjoyment. We also found that trust, followed by compatibility, has a stronger influence on customers’ behavioral intentions in the context of M-payments. This study only included six M-wallets and was restricted to a certain age group in a single city. Understanding the many characteristics of behavioral intention can help M-wallet providers gain consumer trust and increase the frequency with which consumers use M-wallets for M-payments. The findings suggest that M-wallet service providers should consider and manage all influencing elements as proactive strategies for M-wallet intention. This strategy can be used to create an M-wallet-user behavioral intention model that will assist enterprises/companies in managing the establishment of their users’ behavioral intentions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".