Satisfaction Levels of Insured Apricot Producers towards Agricultural Insurance Services
Bibliographic record
Abstract
The objective of this research was to assess satisfaction levels of the insured farmers towards TARSİM agricultural insurance services. The study was conducted on the farmers engaged in apricot production in Malatya province of Turkey, the world’s largest provider of apricots. About 69.88% of Turkey’s dried apricot production and about 73.44% of the world dried apricot production are based in Malatya. Face-to-face interviews were conducted with a random sample of 187 farmers. Likert scale questionnaires were used to collect opinion data of farmers on five dimensions, namely sales and marketing, damage compensation, pricing and payment policy, customer notification and customer representation. Structural equation modeling was used to explore the association between the measured variables and overall satisfaction levels. The results of structural equation modeling indicated that all dimensions had statistically significant effects on farmer satisfaction. Additionally, according to the standard estimations, satisfaction from sales and marketing, satisfaction from customer notification and satisfaction from damage compensation were the most significant determinants of customer satisfaction. Pricing and payment policy had the lowest influence on farmer satisfaction. The study results showed that efficient and rapid resolution of farmer problems and grant of ease for premium payments were the most influential factors affecting farmer satisfaction.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".