Watch Your Mobile Payment: An Empirical Study of Privacy Disclosure
Bibliographic record
Abstract
Using a smartphone as payment device has become a highly attractive feature that is increasingly influencing user acceptance. Electronic wallets, near field communication, and mobile shopping applications, are all incentives that push users to adopt m-payment. Hence, this makes the sensitive data that already exists on everyone's smartphone easily collated to their financial transaction details. In fact, misusing m-payment can be a real privacy threat. The existing privacy issues regarding m-payment are already numerous, and can be caused by different factors. We investigate, through an empirical survey-based study, the different factors and their potential correlations and regression values. We identify three factors that influence directly privacy disclosure: the user's privacy concerns, his risk perception, and the protection measure appropriateness. These factors are impacted by indirect ones, which are linked to the users' and the technology's characteristics, and the behaviour of institutions and companies. In order to analyse the impact of each factor, we define a new research model for privacy disclosure based on several hypotheses. The study is mainly based on a five-item scale survey, and on the modelling of structural equations. In addition to the impact estimations for each factor, our study results indicate that the privacy disclosure in m-payment is primarily caused by the "protection measure appropriateness", which, in its turn, impacted by "the m-payment convenience". We discuss in this paper the research model, the methodology, the findings and their significance.
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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 | 0.015 | 0.077 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".