The democratization of digital currency in Nigeria: A sentiment analysis of eNaira app usability
Bibliographic record
Abstract
Globally, Central Bank Digital Currency (CBDC) is receiving a lot of attention in digital currency deliberations since it is at the top of the agenda of almost every central bank today. Although there is a growing body of literature on CBDCs, user perspectives on their usage and adoption in the literature are very sparse. This study set out to analyze factors impacting the actual usage of CBDC through sentiment analysis of eNaira, the first CBDC to be issued in Africa by the Central Bank of Nigeria (CBN). The research relies on the 214 data points extracted from eNaira App users’ comments from Google Play and App Store from 2020 to 2021. It framed its analysis based on theories of moral sentiments and appraisal theory of emotion. This research shows that eNaira users faced multiple challenges; however, CBN's responses to these challenges are minimal. The results highlighted the need to include public sentiments in policy formulation; the Nigerian government's ban on cryptocurrency trading negatively impacted the uptake of the eNaira due to mistrust. This research study contributes theoretically to appraisal theory and the theory of moral sentiment by explaining the evolution and public sentiments towards adopting the novel eNaira in Nigeria.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.019 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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; a candidate call from one teacher head, 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".