Assessing incentives to increase digital payment acceptance and usage: A machine learning approach
Metadatos
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Plos One
Fecha
2022-11-02Referencia bibliográfica
Allen J... [et al.] (2022) Assessing incentives to increase digital payment acceptance and usage: A machine learning approach. PLoS ONE 17(11): e0276203. [https://doi.org/10.1371/journal.pone.0276203]
Resumen
An important step to achieve greater financial inclusion is to increase the acceptance and
usage of digital payments. Although consumer adoption of digital payments has improved
dramatically globally, the acceptance and usage of digital payments for micro, small, and
medium-sized retailers (MSMRs) remain challenging. Using random forest estimation, we
identify 14 key predictors out of 190 variables with the largest predictive power for MSMR
adoption and usage of digital payments. Using conditional inference trees, we study the
importance of sequencing and interactions of various factors such as public policy initiatives,
technological advancements, and private sector incentives. We find that in countries with
low POS terminal adoption, killer applications such as mobile phone payment apps increase
the likelihood of P2B digital transactions. We also find the likelihood of digital P2B payments
at MSMRs increases when MSMRs pay their employees and suppliers digitally. The level of
ownership of basic financial accounts by consumers and the size of the shadow economy
are also important predictors of greater adoption and usage of digital payments. Using
causal forest estimation, we find a positive and economically significant marginal effect for
merchant and consumer fiscal incentives on POS terminal adoption on average. When
countries implement financial inclusion initiatives, POS terminal adoption increases significantly
and MSMRs’ share of P2B digital payments also increases. Merchant and consumer
fiscal incentives also increase MSMRs’ share of P2B electronic payments.