Why smart cards have failed: looking to consumer and merchant reactions to a new payment technology
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
For more than a decade, bankers and others outside the financial services community such as hardware manufacturers have sought to solidify the place of smart card technology as a viable retail point‐of‐sale alternative and, more boldly, as an outright replacement for cash in everyday consumption situations around the globe. Despite strong development efforts and numerous fact‐finding market trials, many banks have found smart card technology to be a losing proposition. This article presents a detailed case study of both consumer and merchant adoption of one smart card‐based retail point‐of‐sale system. The system, called “Exact”, was test marketed for a full year in the Canadian market. Various perceptual and demographic data from consumers as well as firm‐level data from retailers are both presented and assessed. The ensuing discussion offers pragmatic suggestions for those in the financial services community as to how the apparent difficulties and shortcomings of smart card technology may be overcome.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it