Resistance to Agricultural Commercialization with Lack of Marketing Digital Adoption in Indonesia's Dieng Plateau
Bibliographic record
Abstract
Dieng Plateau is one of the largest vegetable-producing areas in Indonesia, and most of the inhabitants work as farmers.While shifting to commercial agriculture with marketing digital skill can improve farmers' lives, it faces obstacles that cause resistance.The barriers that arise from the commercialization of agriculture in Indonesia are that the scale of agricultural business is generally relatively small, capital is limited, and the use of technology is still simple.Agriculture in the Dieng plateau is seasonal and relies heavily on family labor, with limited access to credit, technology, and markets.Wholesalers and the lack of supply of quality seeds for farmers mainly dominate the market for agricultural products.So, this research explains the obstacles that cause farmers to resist commercialization.The observed barriers included five factors: barriers from factors of production and innovation, such as difficulty adopting digital marketing, lack of relative advantages, lack of compatibility, and complexity.This study tests a constraint model of commercial farming using five factors.The data was collected from 280 farmers who own their land and are not farm laborers.The sampling technique used was purposive sampling with the criteria of individuals having a livelihood as farmers, aged more than 18 years, and owning their land.Data collection uses a questionnaire that has a five-point Likert scale-data analysis technique using PLS-SEM.The results support the hypothesis, suggesting a robust barrier to the commercialization model.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".