IT-Enabled Sustainable Innovation and the Global Digital Divides
Bibliographic record
Abstract
This article investigates the impact of information and communication technologies (ICTs), human capital, institutional settings, socio-economic, and environmental parameters on sustainable innovation (SI) using archival data for 127 economies from 2008 to 2017. We developed an econometrics research framework for investigating factors influencing SI on a global scale. We found that ICT variables, such as ICT access and ICT broadband network, positively influence sustainable innovation in conjunction with the socio-economic and political parameters. Despite differences among economies in terms of ICTs, socio-economic development, and educational attainment, ICTs are the significant drivers of sustainable innovation and economic growth. We observed a growing digital divide among nations within the context of the knowledge-based economy and the expansion of digital commerce, particularly in the least developed countries and Africa, a phenomenon impeding sustainable innovation growth. To the best of our knowledge, this is the first study that empirically investigates the global digital divide from sustainable innovation perspectives. The results of this study suggest that to tackle the digital divide issues, policymakers and educational institutes need to perform constructive educational reform in higher education curricula, particularly concerning STEM programs, which should reflect the necessary skills and competencies for deploying emergent technologies. In addition, ICT should be considered part of a country’s critical infrastructure, particularly investment in the broadband networks regarded as the backbone of today’s innovation.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".