Qualitative study examines digital financial technologies' effect on financial inclusion and socioeconomic outcomes in rural communities, suggesting barriers and strategies for improvement.
This qualitative study examined the impact of Digital Financial Technologies (DFTs) on financial inclusion and socioeconomic outcomes in Matabeleland South Province, Zimbabwe. Guided by an integrated theoretical framework combining the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT), the study sought to assess opportunities for inclusion, identify adoption barriers, and understand related socioeconomic effects among rural communities. Data were collected from rural community members and community leaders across four districts Beitbridge, Gwanda, Umzingwane, and Maphisa using purposive sampling stratified by age group (youth aged 20–30 and older adults aged 50–60). A sample size of 36 participants was selected to provide qualitative data through interviews and focus groups. Semi-structured interviews and focus group discussions were conducted between 2020 and 2025, and qualitative data were analyzed using thematic analysis with iterative coding. Ethical procedures were followed to ensure confidentiality, informed consent, and participant rights. Results revealed seven major themes. While participants recognized potential benefits from DFTs such as improved savings practices, faster transaction times, and new business opportunities overall awareness and understanding of DFTs were low, especially among older adults. Trust emerged as a recurring factor influencing adoption, shaped by fears of fraud, data misuse, and the limited availability of human support for complex issues. Key barriers included low digital literacy, language constraints, poor network coverage, frequent power failures, and high data costs, which disproportionately affected women, the elderly and lower-income groups. Stakeholder strategies such as agent banking and digital literacy programs were viewed as promising, though existing AI-focused solutions were often designed for urban users and did not adequately address rural needs.
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Arnold Maviya2* Rethakgetsi Victoria Silaigwana1* (2026) studied this question.
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