Digital Drivers and Economic Performance in Nigeria: A Disaggregated Analysis of ICT Value-Added, Mobile Broadband and Internet Penetration (2005-2024)
Bibliographic record
Abstract
The study investigates how the digital drivers has affected Nigeria's economic performance between 2005 and 2024. The study specifically, examined the effect of ICT value-added, internet penetration, and mobile broadband on economic performance in Nigeria. The study used secondary data; Data were collated from the World Bank, Nigerian Communications Commission, Central Bank of Nigeria, and National Bureau of Statistics. The study employed panel data analysis. The study reveals that ICT value-added has positive effect on economic performance in Nigeria with coefficient value of 40.55 (p>0.001), mobile broadband has positive effect on economic performance in Nigeria with coefficient value of 5.39 (p <0.01), whereas internet penetration showed no meaningful influence on economic performance in Nigeria with coefficient value of 0.13 (p=<0.01). The study also found significant variances in ICT value-added, internet penetration, and mobile broadband, while GDP remained relatively consistent. Strong positive correlations between GDP and digital indicators (ICT, IP and MB) with value of 0.7327, 0.89946 and 0.92334 respectively. The study concludes that digital drivers have positive effect on economic performance in Nigeria, especially when economic performance is measured with GDP. The study recommends that in order to maintain inclusive economic performance, the government should boost digital entrepreneurship, expanding broadband, and fortifying ICT regulations in the country. Keywords: Digital Drivers, Economic Performance, ICT Value-Added, Mobile Broadband, Internet Penetration Proceedings Citation Format Ayodeji Ojo Oloruntuyi, Festus Femi Ogunyemi & Bamikole Samson Fajuyagbe (2025): Digital Drivers and Economic Performance in Nigeria: A Disaggregated Analysis of ICT Value-Added, Mobile Broadband and Internet Penetration (2005-2024). Proceedings of the 39th iSTEAMS Multidisciplinary Bespoke Conference & 39th Extended Conference 17th – 19th July/17th – 19th October, 2025. University of Ghana, Accra, Ghana. Page 376. https://www.isteams.net/ghana2025. dx.doi.org/10.22624/AIMS/ACCRABESPOKE2025P39
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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 | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.010 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".