Are There Social Spillovers in Consumers’ Security Assessments of Payment Instruments?
Bibliographic record
Abstract
Even though security of payments has long been identified as an important aspect of the consumer payment experience, recent literature fails to appropriately assess the extent of social spillovers among payment users. We test for the existence and importance of such spillovers by analyzing whether social influence affects consumers' perceptions of the security of payment instruments. Based on a 2008-2014 annual panel data survey of consumers, we find strong evidence of social spillovers in payment markets: others' perceptions of security of payment instruments exert a positive influence on one's own payment security perceptions. The significant and robust results imply that a consumer's assessments of security converge to his peers' average assessment: a 10 percent change in the divergence between one's own security rating and peers' average rating will result in a 7 percent change in one's own rating in the next period. The results are robust to many specifications and do not change when we control for actual fraud or crime data. Our results indicate that spillovers rather than reflection appear to be the cause, although separating the two causes is very difficult (Manski 1993). In particular, the spillovers are stronger for people who experience an exogenous shock to security perception, people who have more social interactions, and younger consumers, who are more likely to be influenced by social media. We also examine the effects of social spillovers on payment behavior (that is, on decisions regarding payment adoption and use). Our results indicate that social spillovers have a rather limited impact on payment behavior, as others' perceptions seem to affect one's own payment behavior mainly indirectly through the effect on one's own perceptions.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".