Examining the Factors Influencing Mobile Banking Adoption in Somalia: A Quantitative Study
Bibliographic record
Abstract
Somalia has a large mobile money user base, with over 83% of mobile phone users using mobile money services.Compare this to the penetration rate of financial services, which is just 5% among rural populations and 1% among internally displaced people.This paper seeks to determine the factors influencing mobile banking adoption in Somalia Through a comprehensive survey involving 130 participants, integrating online as well as face-to-face approaches was employed.The data that was collected that subsequently assessed employing the SPSS software, facilitating a thorough investigation into the subject.This research reveals that perceived ease of use, perceived usefulness, and trust significantly influence individuals' intentions to adopt mobile banking services in Somalia.Focusing on enhancing the user experience, emphasizing practical benefits, and fostering trustworthiness emerges as crucial strategies for boosting mobile banking adoption rates.Notably, perceived cost and risk play a lesser role, possibly owing to cost-effective transaction options and high trust in the reliability of mobile money services.These findings emphasize actionable steps for mobile banking stakeholders in Somalia, pointing towards the importance of user-centric enhancements, clear communication of practical advantages, and continued efforts to build and maintain trust in the mobile banking ecosystem.Based on these findings, a recommendation would be to focus on improving the user experience of mobile banking applications in Somalia.Furthermore, collaboration between mobile network operators, financial institutions, and the government could foster a supportive environment for mobile banking adoption, leading to increased accessibility and usage for the broader population in Somalia.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".