ICT adoption, innovation and financial development in a digital world: empirical analysis from Africa
Bibliographic record
Abstract
As a response to a digital era that is seen as a core element of global digitalisation, financial development in many countries including those in the African continent experienced varying patterns. These have made some countries to be categorised as relatively high and others relatively low in terms of their scale on digital economy. Thus, this study empirically investigates the interaction of information and communication technology (ICT) adoption and innovation, and the role of this digitalisation interaction on financial development in Africa, and across the sub-regions. It utilises the Bayesian Vector Auto-Regressive (BVAR) modelling to simulate the impulse response function and variance decomposition across Africa. The study finds that ICT-innovation interaction shock positively drives financial development across all of 6 datasets. This implies that for multinational corporations (MNCs) and other economic agents, ICT – innovation interaction should be strongly applied across all sectors to drive financial development since all sectors require finances to improve performance. Thus, contributes to the empirical testing of the theoretical reflections of digitalisation and digital economy interaction in African countries.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.011 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".