Investigation of Effective Factors on Consumers’ Desire for Organic Products Payment, Case Study: Kermanshah Province Agricultural Bank Staffs
Bibliographic record
Abstract
The present research’s main purpose is to evaluate the effective factors in customers desire to pay for organic products. The method of research is practical based on the goal of research and due to lack of complete control of research variables is none experimental, based on the data identity, data are qualitative and based on gathering method, data are provided in the field of study and about the feature of research it is communicative-visual kind. The statistical society contains all staffs of Kermanshah province agricultural bank staffs containing 418 people among which 200 peoples selected by the Cochran sample volume determination formula as the statistical sample and by random sampling method and by drawing. The tool to gather information in this research contains a researcher answer sheet whose justification confirmed by the special panel of agricultural training and privilege in Ilam Azad university agricultural management. The research stability obtained through the Cronbach’s alpha at 0.87. The dependant variable also declined to pay for the organic production and dependant variables containing individual features, knowledge to organic production, on time informing and the customer expected features, cultural factors and economical issues. The data analysis by the SPSS software and in two level of descriptive statistics (the central and distributed scales) and analytic statistic correlation coefficient for connecting theories, the average comparing test for the theories comparison and also multiple regressions performed step by step. The results showed that the relation between the economical factors and desire to pay for organic products has positive meaningful relation. The results showed that the economical factors and price, cultural factors and connecting factors to advertisement and informing have the maximum effect on desire for payment by customers for organic products.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".