Analyzing the community decision making to purchase pet insurance: Case study of animal lovers in Indonesia
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study aims to measure people's decision-making to buy their pet insurance and compare it with the amount of insurance premium rates offered. It is important due to the increase in people's income which has triggered the birth of a community of pet lovers as part of the middle-class people’s lifestyle in Indonesia. The survey data was conducted using the Stated Preference (SP) format through questionnaires and interviews to determine the public response to pet insurance premiums. The collected data were analyzed using descriptive methods, decision-making analysis was on the basis of the choice of the dichotomous Contingent Valuation Method (CVM), and logistic regression analysis. Based on the calculation analysis using the logit method shows that the ability of the public to pay pet insurance premiums is IDR289,454.54. Analysis of calculations using the Turnbull method was obtained at IDR365,000.00. The results of the WTP amount, both using the logit method and using the Turnbull method, are greater than the minimum premium amount offered which is IDR190,000.00. The results of this study indicate that the premium rates for pet insurance offered are still within reasonable limits, compared to the size of the decision-making by the animal lover community in Indonesia. This provides a very good prospect for insurance companies that have insurance products for pets in Indonesia. This study was conducted to provide empirical evidence that the decision-making of the animal lover community is greater than the premium rate for pet insurance that has been offered. Thus, this research strongly supports the development of pet insurance companies in Indonesia, which can provide pet protection to stay healthy and well looked after.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it