Effects of Supplier’s Competitive Factors on Relationship Performance and Product Recommendation in Crop Protection Retail Sector
Bibliographic record
Abstract
The changes in distribution channels of the crop protection industry are accelerating the influence of crop protection retailers on farmers’ product purchase decisions. This study aims to identify the critical competitive factors; ‘product quality’, ‘supply price’, ‘brand awareness’, ‘flexibility’, and ‘promotion support’; of crop protection manufacturers. And it empirically analyzes effects of the critical factors on relationship performance and product recommendation of crop protection retailers. This research also examined the difference among these major factors according to the level of trust of crop protection companies as suppliers. Survey data were collected from 660 retailers by the crop protection distribution market in South Korea. As for the results, the five factors were defined as the crop protection suppliers’ competitive factors. Supply price, promotion support, brand awareness, and flexibility had a positive (+) effect on relationship performance. Brand awareness, promotion support, product quality, and flexibility had a positive (+) effect on customer recommendation. Furthermore, supply price significantly affected relationship performance in a group with high trust, and promotion support significantly affected a group with low trust.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".