Exploring individuals' socioeconomic characteristics and digital infrastructure determinants of digital payment adoption in Ethiopia
Bibliographic record
Abstract
The COVID-19 pandemic has spurred a surge in digital payments, with over 40 % of adults in low- and middle-income countries making their first merchant payments using cards, phones, or the internet since the pandemic began. This study examines the determinants of adopting digital payments during the COVID-19 pandemic in Ethiopia. To achieve this objective, the study utilized secondary data from the World Bank's most recent dataset, collected as part of the Global Findex Database 2021. A Covariance Based-Structural Equation Modeling (CB-SEM) was applied to analyze the data and explore the intricate pathways between variables. The mediation role of the use of technological tools on the relationship between socio-economic factors and digital payment adoption was also examined. Accordingly, mobile ownership, having an ATM/Debit card, and internet access were the technological tools significantly determining digital payment adoption. Age, education, income quantile, receiving wage payment, and engagement in formal financial transactions (as a proxy for financial inclusion) were among the socio-economic characteristics influencing digital payment adoption. Engagement in formal financial inclusion , mobile ownership, having an ATM/Debit card, and internet access were also influenced by individuals' socio-economic characteristics. Mobile ownership, having an ATM/Debit card, internet access, and engagement in formal financial transactions were significant mediating factors in the relationship between digital payment and socio-economic predictors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.004 | 0.017 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".