Table 1

Selection of research exploring socio-economic variables and electronic/mobile banking adoption

StudySocio-economic variablesImpact on electronic/mobile banking adoption
Chawla and Joshi (2018) Age, Education, Gender, Experience, Income, Marital status and OccupationThose who were married and younger professionals with 3–5 years work experience were more likely to adopt. Education and Gender did not have a significant affect on adoption
Kolodinsky et al. (2004) Age, Education, Income, Ethnicity, Marital statusYounger (less than 35 years), better educated with higher income were more likely to adopt. Marital status had limited impact and ethnicity did not significantly affect adoption
Laforet and Li (2005) Age, Education, Gender, Income, OccupationIndividuals aged 35–44 with higher income and work experience were more likely to adopt. Women less likely to adopt. Education did not have a significant affect on adoption
Laukkanen and Pasanen (2008) Age, Education, Gender, Household size, Income, OccupationIndividuals aged 30–39 and 40–49 were more likely to adopt; women less likely to adopt. Education, Household size, Income, and occupation did not affect adoption
Malaquias and Hwang (2019) Age, GenderAge and Gender did not have a significant affect on adoption
Sohail and Al-Jabri (2014) Age, Cultural differences, Education, Gender, Income, OccupationIndividuals aged 18–25, with higher education were more likely to adopt; women less likely to adopt; Culture had limited affect. Income did not significantly affect adoption

Source(s): Authors’ own work

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