Adoption of ICT4D and its determinants: A systematic review and meta-analysis
Bibliographic record
Abstract
Various Digital Agricultural Technologies (DAT) have been developed and implemented around the world. This study aims to estimate the overall adoption rate and identify the determinant factors for a better adoption perspective after decades of innovation and dissemination. A systematic review was conducted on published studies that reported adoption rates and determinant factors using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol. We used meta-regression and the partial correlation coefficient to estimate the effect size and establish the correlation between socioeconomic characteristics and the adoption of various technologies reported. Fifty-two studies with 32400 participants met the selection criteria and were included in the study. The results revealed an overall pooled adoption rate of 39 %, with the highest adoption rates in developing countries in Africa and South America. Socioeconomic factors such as age, education, gender, and income were found to be the main determinants and should be considered when designing technology for sustainable adoption. The study also found that young farmers were more susceptible to adoption. Moreover, farmers with higher income levels and educational attainment are more likely to use technology linked to agricultural production, market access, and digital advising, implying that high-income farmers with more education are more tech-savvy. However, this does not exclude low-income and low-educated farmers from adopting the technologies, as many models and strategies with socioeconomic considerations were developed. It is one of the reasons behind the underlying enthusiasm for digital agricultural adoption in low and middle-income countries.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.009 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".