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Record W4205844696 · doi:10.26686/wgtn.16985710

Development of Retail Payment Systems Since 1949

2011· dissertation· en· W4205844696 on OpenAlexaboutno aff
Michael R. Wilkinson

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldBusiness, Management and Accounting
TopicDigital Platforms and Economics
Canadian institutionsnot available
Fundersnot available
KeywordsPaymentBusinessDatabase transactionPayment cardPayment service providerCeteris paribusPayment systemTransaction costIncentiveCommerceProfit (economics)MarketingEconomicsFinanceComputer scienceMicroeconomics

Abstract

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<p>Starting with the introduction of the Diner's Club payment card in 1949, the means of exchange have progressed well beyond traditional instruments such as notes, coins and cheques. I use institutional economics to analyse historical data on the evolution of recently-developed retail payment systems in Australia, Canada, Germany, New Zealand, Norway, the United Kingdom and the United States. The framework I create yields insights into the incentives faced by the users of payment instruments and the payment networks that provide them. It also provides a means to assess the role of government in the evolution of retail payment systems. Ceteris paribus, consumers and merchants will prefer low transaction cost payment instruments. In order to complete a transaction, a consumer will proffer an instrument that may or may not be accepted by the merchant. Together, merchants and consumers will choose the payment instrument that generally reduces demand-side – i.e. consumer and merchant – transaction costs, relative to other available instruments. Consumer irreversible costs of adoption enhance the importance of network effects. To help overcome these, I argue payment networks need to make acceptance of their instrument attractive to merchants, which I find to be supported by analysis of the pricing of payment instruments. It distinguishes recently-developed payment instruments from other new technologies – the most technologically advanced instrument will not likely be adopted unless it is first acceptable to merchants. In workably competitive conditions, profit-seeking payment networks will attempt to provide an instrument that gets used while it at least recoups its costs of supply from fees paid by users. I argue this suggests a process of institutional adaption for profit-seeking payment networks. Network effects mean the use of an instrument grows disproportionately faster, the greater the number of people using it. For instrument supply, this means profit-seeking payment networks have an incentive to increase participation. In the presence of potential inter-network competition, a payment network will likely experience greater participation if, ceteris paribus, it offers an instrument that generally reduces demand-side transaction costs to a greater degree than competing networks' instruments and provides it with lower costs of supply. Governments play two key roles in retail payment system development. First, they can affect the development of systems by how well they protect property rights and enforce contracts. Although this role is performed relatively well in my sample countries, my analysis suggests that the use of recently-developed retail payment systems would fall, substantially, were it not so. Second and more importantly in my sample countries, governments impose restrictions on the freedom of contract for payment networks. If restrictions on this freedom are such that they prevent the trading of property rights, they risk reducing either the demand or the supply of payment instruments. Such restrictions might reduce demand if the instrument that would have been used no longer generally reduces demand-side transaction costs. They might reduce supply in two ways: by impeding payment networks' attempts to offer instruments that reduce these transaction costs or by reducing inter-network competition. In summary, I find that it is government restrictions on the freedom of contract that cause the substantial differences in the use of newly-developed retail payment systems between my sample countries. By risking reducing the supply and demand of retail payment systems, these restrictions may diminish innovation in payments, thereby harming dynamic efficiency.</p>

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient 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: none
Teacher disagreement score0.949
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0000.000
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 head0.033
GPT teacher head0.200
Teacher spread0.167 · 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".

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Citations0
Published2011
Admission routes1
Has abstractyes

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