Realising M-Payments: modelling consumers' willingness to M-pay using Smart Phones
Bibliographic record
Abstract
It is predicted that significant and ongoing investment in M-Commerce platforms and application development by commercial entities will fundamentally change consumers' shopping and web browsing behaviours. However, the evolving behaviour of Smart Phone users is somewhat tempered by concerns over M-Payments. If Smart Phones are to reach their full M-Commerce potential, the ability of consumers to transact and pay for products/services through these devices in an easy, safe and reliable manner must be addressed. In response, this paper contributes a theoretical model and empirically tests the model to explore Irish consumers' perceptions of using Smart Phones to make M-Payments for products/services. The findings present conclusive evidence that trust is the most powerful factor influencing consumers' willingness to use Smart Phones to make M-Payments. While perceived usefulness and perceived ease of use influence the payment decision, their impact is much lower. Mobile self-efficacy and personal innovativeness have almost no direct impact. The paper concludes that irrespective of individuals' high levels of personal innovativeness or mobile self-efficacy and irrespective of whether Smart Mobile Media Services are perceived as useful and easy to use, consumers will not make M-Payments, until they are convinced that Smart Phone M-Payment systems are safe and reliable.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".