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Record W7019296998

Fysiska eller digitala betalningar : Mot en dominant design?

2018· article· en· W7019296998 on OpenAlexaboutno aff

Bibliographic record

VenueKTH Publication Database DiVA (KTH Royal Institute of Technology) · 2018
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicDigital Platforms and Economics
Canadian institutionsnot available
Fundersnot available
KeywordsPaymentInfluencer marketingNetwork effectPerspective (graphical)Payment systemDistribution (mathematics)
DOInot available

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.854
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.008
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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.

Opus teacher head0.024
GPT teacher head0.229
Teacher spread0.205 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2018
Admission routes1
Has abstractyes

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