Fysiska eller digitala betalningar : Mot en dominant design?
Bibliographic record
Abstract
Rapid digitalisation development has been stampeding widely across today’s societies, and not least in the payment industry. Though, the digitalisation in the payment industry has been very deviating, even between similar well-developed countries, and while there are positive and negative effects with both digital- and physical payment means, there is little knowledge that highlights the influencing factors and accompanied problems. This study therefore explore swhich, and how, different factors influence a country’s degree of digital payments, and creates further understanding of where the payment markets are heading in the future. It is done through a case study of four different industrialised countries, Sweden, Italy, Canada, and Switzerland which involves mapping the countries’ payment markets, as well as potential factors influencing a population’s payment habits, through a perspective of innovation theory in terms of dominant designs and technological discontinuities. Theory of network externalities and two-sided platforms are further used to explain and discuss how a two-sided market, likethe payment market, is affected by changes and other circumstances in different ways.Conclusions are then drawn from the used theories together with a comparison of the findings,and identifies certain influencers to a country’s distribution of payments, as well as provides indications of where the different payments markets are heading in the future. Data is mainly gathered through written material and credible databases, but also from semi-structured interviews.
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.
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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.019 | 0.011 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".