Socio-demographic Determinants of Mobile Technology Use and Digital Inclusion among Rural Women in Ondo State, Nigeria

Abstract

Aims: This study aimed to examine the socio-demographic determinants of digital inclusion and mobile technology use among rural women in Ondo State, Nigeria. Specifically, it assessed whether factors such as age, marital status, household size, number of children, education, religion, ethnicity, and income-generating activities predicted functional digital engagement. Study Design: A cross-sectional survey design was employed. Place and Duration of Study: Data were collected in rural communities of Ondo State, Nigeria, in June 2025. Methodology: A total of 120 women were surveyed using structured interview schedules capturing socio-demographic characteristics, digital usage patterns, and livelihood activities. The dependent variable was a digital inclusion index derived from reported use of mobile phone functionalities (internet browsing, WhatsApp voice/video, multimedia sharing). Independent variables included age, household size, number of children, education, marital status, religion, ethnicity, and livelihood income. Descriptive statistics, chi-square tests, Pearson’s correlations, ANOVA and linear regression model were applied to test associations. Results: The sample was predominantly married (65%), Christian (88.3%), and Yoruba (89.2%), with a mean age of 38.4 years. The OLS regression showed weak explanatory power (R² = 0.049), with no socio-demographic predictor reaching conventional significance (p < .05). However, religion (Christian, b = –0.376, p = .063) and ethnicity (Yoruba, b = –0.342, p = .079) demonstrated marginal negative associations with digital usage. Among livelihood activities, petty trading was significantly and negatively correlated with digital inclusion (r = –0.534, p = .049). Conclusion: Digital inclusion among rural women in Ondo State is not strongly patterned by socio-demographic variables but appears more influenced by structural and socio-cultural constraints. Interventions should therefore prioritize improving affordability, access, and digital literacy while tailoring training to livelihood groups.