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Demographic characteristics have been critical in research because they can be used as direct predictors of outcome variables or mediators and moderators across disciplines (Abdinoor and Mbamba, 2017; Yasir, Liren, Mehmood, and Arfat, 2019; Stokes et al. 2020). Age and wealth are two demographic variables that paint a vivid picture of human populations in a specific situation. Additionally, it demonstrates the structure and socioeconomic change in society and the relationship between these variables and their natural surroundings. The GINI index indicates that income inequality in Nigeria is 39%, with 0 representing a disparity and 100 indicating perfect equality (Knoema, 2018). The GINI index measures how income is distributed in Nigeria, higher than in South Africa (57.7%) and lower than in advanced countries such as Finland (25.6%). The banking sector must consider the disparity in income distribution and age differences, as these characteristics influence the usage of mobile technologies for banking transactions.
The banking industry is currently in the era of big data, yet it is underutilizing this opportunity (Razzaque et al., 2020; Derbali, 2021; Turki et al., 2020). They could not transform their data into the intelligence information necessary to decide how to develop their business. Several scholars have explored the relationship between age and wealth from different angles, but the application of age and wealth to the field of mobile banking research is limited. While banks interact with their customers weekly, few pay close attention to demographic aspects, particularly technological use. This study identified a gap in applying demographic characteristics such as consumer age and income for effective targeting and segmentation of mobile banking apps.
The purpose of this study is to classify the relationships between age and mobile banking app usage. Also, this study investigates the income, the type of device, and the brand of device used for a mobile banking app. Additionally, this study incorporated Social Stratification Theory and quantitative techniques such as the Chi-square test and discriminant analysis via SPSS ver. 25. The purpose of this study is to address the following research question: How can the banking industry profit from the insight gained from social-economic variables using mobile banking apps?
One study examined the relationship between individual awareness, perceived usefulness, benefit, cost effect, and intention to embrace mobile financial services using age, sex, income level, and education level. The categorical variable mediation demonstrates the degree of significance and insignificance of the relationship between the independent and dependent variables (Abdinoor and Mbamba, 2017). Estacio, Whittle, and Protheroe (2019) found that social-demographic indicators such as age and income influence Internet access, but Kalimeri, Beiró, Delfino, Raleigh, and Cattuto (2019) saw demographics as a way of drawing a portrait of evolving cyber-cultures. Additionally, Olaleye, Sanusi, Ukpabi, and Aina (2017) investigated Millennials' smartphone usage with a focus on operating systems, text messaging, Wi-Fi, Internet surfing, and social media, and their findings demonstrate the value of profiling in a developed market based on five levels of Millennial clustering.