Who pays for preparedness? Valuing disaster app features through a factorial survey experiment in flood-prone communities
Bibliographic record
Abstract
This study evaluates public preferences for next-generation disaster preparedness apps using a factorial survey experiment in flood-prone Japanese communities. The analysis first reveals a fundamental heterogeneity in public receptiveness, identifying two distinct segments: a small "receptive" minority (approx. 20%) willing to consider adoption, and a large "unreceptive" majority (approx. 80%) that rejects the app regardless of its features or price. Consequently, focusing on the receptive segment, the study estimates the economic value of specific app features. Results show that functions for immediate personal safety and family security—such as Rescue Request and Family Status Confirmation—are most highly prized. These findings lead to the conclusion that a freemium model is the most viable strategy for social implementation, offering a free version with basic features to the unreceptive majority while providing a premium, feature-rich version to the receptive minority at a sustainable price point. This dual approach can maximize public reach while ensuring financial viability.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.001 | 0.000 |
| 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".