Understanding Consumer Resistance to the Consumption of Environmentally-Friendly Agricultural Products: A Case of Bio-Concentrated Liquid Fertilizer Product
Bibliographic record
Abstract
Methane fermentation digested sludge is a sustainable resource that is used as a liquid fertilizer. An innovative liquid fertilizer called Bio-Concentrated Liquid Fertilizer (Bio-CLF) was developed to solve the problems such as high transportation cost associated with current liquid fertilizer. As an innovation product, Bio-CLF inevitably creates remarkable resistance from consumers. Hence, we used the Innovation Resistance Theory (IRT) to determine the reasons for consumer resistance to Bio-CLF products. A total of 2,000 samples from three major cities, including Tokyo, were extracted via the Internet, and 703 samples were finally selected for analysis. Perceived risk, complexity, and attitude toward existing products were found to have a positive and direct impact on consumer resistance to Bio-CLF products, while motivation and purchase intention were found to have a negative and direct impact on consumer resistance to Bio-CLF products. Notably, Relative Advantage and Compatibility had a positive impact on motivation and indirectly influenced consumer resistance to Bio-CLF products, the results of which are inconsistent with IRT, as those characteristics could have a direct influence on resistance. Additionally, we opted to provide some advice that for market managers: (1) allocate a specialized corner for the Bio-CLF product and (2) place the Bio-CLF product alongside other green products. For producers: (1) disclosure of production information; (2) design of an attractive and clear label sheet; (3) proving the advantage of Bio-CLF and that the Bio-CLF product is a green product.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 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".