2020
DOI: 10.1007/978-3-030-64849-7_48
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Explaining Variation in Adoption of FinTech Products and Services Among Citizens: A Multilevel Model

Abstract: Despite the several advantages, adoption of the FinTech services vary across individuals in different countries. This study proposes a model to examine how the country level ICT competitiveness, demographic and socio-economic factors influence the FinTech services diffusion in a country. In this study, we use Generalized Linear Mixed Model to analyze both the individual-level and country-level variables to explain the variation in the probability of individuals adopting FinTech services while accounting for th… Show more

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Cited by 4 publications
(3 citation statements)
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“…Our results also support the third hypothesis. In tandem with Krishna and Krishnan, 66 more technologically driven countries with advanced and smart ICT systems more likely to stimulate financial technologies to influence a firm’s sustainability.…”
Section: Resultsmentioning
confidence: 99%
See 1 more Smart Citation
“…Our results also support the third hypothesis. In tandem with Krishna and Krishnan, 66 more technologically driven countries with advanced and smart ICT systems more likely to stimulate financial technologies to influence a firm’s sustainability.…”
Section: Resultsmentioning
confidence: 99%
“…Studies also find that ICT development is positively associated with financial inclusion and income equality. 13 , 63 - 65 Krishna and Krishnan 66 used a generalized linear mixed model to analyze the country-level and individual-level factors that influence the adoption of FinTech services. They assert ICT competitiveness as a prerequisite for FinTech services diffusion in a country.…”
Section: Theoretical Framework and Hypotheses Developmentmentioning
confidence: 99%
“…As for the second line of research it assessed adoption on the consumer level and studied the factors affecting their decisions to accept and use Fintech services (Hua et al, 2019). Most of these studies had relied on technology adoption literature to build their research models (Krishna & Krishnan, 2020), such as Technology Acceptance Model (TAM) (Davis, 1985;Davis et al, 1989). Additional variables such as social influence, responsiveness, security (Singh et al, 2021), government support, perceived risk, brand image, and user innovativeness (Hu et al, 2019), were added to increase the predictivity power of the tested models (Singh et al, 2021).…”
Section: Literature Reviewmentioning
confidence: 99%