Risk management in the adoption of smart farming technologies by rural farmers
Bibliographic record
Abstract
Smart farming is a feasible solution to help farmers effectively and sustainably manage the potential threats and risks those traditional farmers face, such as product quality, increased production costs, the environment, climate change, natural catastrophes, pests, and inferior goods. Using a survey research design, this research examined smart farming adoption and risk management models by combining the Technology Acceptance Model (TAM) and the Innovation Diffusion Theory (IDT). The research sampled 400 farmers who are members of community enterprises in the northeastern region of Thailand. Data was collected using a questionnaire and analyzed using a statistical package program in four steps: confirmatory factor analysis, path analysis, structural equation model analysis (SEM), and Sobel's test. The findings revealed that government support variables had the most significant influence by adopting smart farming to risk management. Based on the research results, the government can apply this model to create strategies to encourage farmers to adopt smart farming and increase the production efficiency of agricultural products. The farmer can manage the risks of smart farming, which leads to sustainable smart farming and is useful for further academic acceptance and risk management studies. Furthermore, this study contributes to the existing literature on combining TAM and IDT in model adoption and risk management. The limitations include the small sample size adopted and the limited coverage area for the study, which restricts the generalization of the findings. However, the findings offer a glimpse into the benefits of smart farming.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".