Who pays for preparedness? Valuing disaster app features through a factorial survey experiment in flood-prone communities
Bibliographic record
Abstract
Floods are among the few natural hazards that allow for anticipatory evacuation, making timely information and action critical to reducing casualties. Mobile disaster preparedness applications (apps) have emerged as promising tools to support such decisions, yet their adoption remains limited, and their design features poorly understood. This study investigates public preferences for next-generation disaster apps by evaluating eight advanced features aimed at facilitating flood evacuation. Using a factorial survey experiment (FSE) with 1,371 residents from 16 flood-prone municipalities in Ibaraki Prefecture, Japan, we estimate both the marginal willingness to pay (MWTP) for individual features and the expected willingness to pay (EWTP) for bundled app configurations. Mixed-effects logit models reveal stark heterogeneity: while a receptive minority exhibits consistent and economically meaningful demand, the majority consistently rejects app adoption regardless of feature content or price. Within the receptive group, five features—rescue request, family status confirmation, evacuation route guidance, shelter information, and damage reporting—garner strong support. The close alignment between aggregated MWTP and EWTP in this group confirms the internal validity of our valuation. These findings highlight the value of user-centred design in disaster app development and offer practical insights for extending adoption among currently disengaged populations.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".