Abstract:

Adoption factors of Financial Technology (Fintech) services have been the subject of investigation in a growing body of extant literature. Macro-level as well as user-specific factors that contribute to the adoption of customer-facing fintech services have been studied. Emerging market studies mostly considered targeted demographic and socio-economic segments, limiting their ability to reflect a wide spectrum of relevant factors. We conducted a nationwide representative survey of 1282 individuals in Bangladesh. A total of 16 administrative districts from all 8 administrative divisions were included. Addressing sample imbalance with Synthetic Minority Oversampling Technique (SMOTE), we deployed Recursive Feature Elimination (RFE) to reduce number of customer features down to the most important. Using Library of Large Linear Classification (LIBLINEAR) for multivariate Logistic Regression, we identified significant features that predict customer-facing fintech adoption among individual respondents. We found that customers were less likely to adopt fintech services if they had higher reported levels of concern with security, information secrecy, limited government control, and high levels of reported service intuitiveness obstacles. Our evidence suggests these concern factors constitute the prominent factor behind fintech adoption, as opposed to demographic variables, for example. Our findings hold insights for fintech services providers and policy makers.

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