The impacts of observational learning and word-of-mouth learning on farmers’ use of biogas in rural Hubei, China: does interpersonal trust play a role?
Bibliographic record
Abstract
Abstract Background Residue-based biogas is considered as a renewable energy that should be used to improve energy security and household livelihoods in rural areas. Observational learning and word-of-mouth learning are critical in the dissemination of knowledge about agricultural technologies. Yet, scholars have little understanding of the impacts of these learning methods on farmers’ use of residue-based biogas. Using survey data from rural areas of Hubei China, this study estimates the impacts of observational learning and word-of-mouth learning from different subjects (i.e., relatives, neighbors, cadres, cooperative members, and technical instructors) on the use of residue-based biogas by farmers. Additionally, the moderating role of interpersonal trust in these relationships is explored. Results Results from logistic regression models show that observational learning from technical instructors significantly increases farmers’ use of biogas. Furthermore, interpersonal trust significantly and positively influences the impact of observational learning on farmers’ decisions to use biogas. Similarly, interpersonal trust significantly and positively moderates the influence of positive word-of-mouth learning on farmers’ decision to use biogas. In contrast, a negative moderating role exists in the relationship between negative word-of-mouth learning and farmers’ decision to use biogas. These impacts are further affirmed by robustness checks. Conclusions The results presented here show that enhancing farmers’ interpersonal trust promotes the use of residue-based biogas by farmers. One important implication is that the government might promote the use of residue-based biogas by organizing technology demonstration activities, providing communication platforms, and enhancing mutual trust between farmers and relevant groups.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".