Factors shaping innovative behavior: A meta‐analysis of technology adoption studies in agriculture
Bibliographic record
Abstract
Abstract This study conducts a meta‐regression analysis to synthesize the marginal effects of 12 factors that frequently appear in empirical studies examining farmer's technology adoption behavior. The analysis includes 187 observational studies on technology adoption in agriculture, which are published in 32 peer‐reviewed journals in the broader field of agricultural economics, covering farmer's adoption in 47 countries for a diverse range of agricultural technologies. Using this broad meta‐dataset, we investigate whether each of the 12 determinants has a true effect on technology adoption rates and examine whether Type I and Type II publication bias are present in the adoption literature. Our results reveal that while most determinant factors significantly affect adoption rates, their marginal effects are generally of small magnitude and vary considerably by technology type and country group. Additionally, our results provide evidence of the presence of Type I publication bias in half of the factors considered and Type II publication bias in nearly all, underscoring the need for caution when interpreting results in the adoption literature by researchers and policymakers. Overall, the findings highlight the critical need for proactive measures to address publication bias and promote more transparent and credible research practices in agricultural economics.
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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 | 0.050 | 0.132 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.032 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".