The Consumer Value Proposition for a Hypothetical Digital Canadian Dollar
Bibliographic record
Abstract
Research into a hypothetical Digital Canadian Dollar has largely focused on public policy, financial technology innovations and public opinion. In this study, we explore the consumer value proposition of a hypothetical Digital Canadian Dollar, considerations for its adoption and the users who would benefit most from this potential new payment method. We employ a design-thinking consultation methodology, allowing participants to interact with research prototypes of increasing complexity to reveal user preferences, constraints, and adoption influences. Qualitative insights are corroborated using quantitative, large-population surveys and contrasted with results from a Bank of Canada open online public consultation. We find that most participants would support the issuance of a hypothetical Digital Canadian Dollar, and we identify the segments most likely to be early adopters. However, broad early adoption is unlikely given that available payment methods meet the needs of most users. Financially vulnerable segments often have the most to gain from this new payment method but are most resistant to adoption. Important considerations for appeal and adoption potential include universal merchant acceptance, low costs, easy access, simplified online payments, shared payment features, budgeting tools, and customizable security and privacy settings. Participants cited these features far more often than offline functionality and the ability to make anonymous payments. Our results also show that cash remains an important method of payment and that certain groups may strongly resist a Digital Dollar if they conflate its launch with the end of cash issuance. We find a hypothetical Digital Canadian Dollar requires the support of an information campaign to be understood, valued and adopted.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".