Willingness of Hurricane Irma evacuees to share resources: a multi-modeling approach

Recent technological improvements have expanded the sharing economy (e.g. Airbnb, Lyft, and Uber), coinciding with a growing need for evacuation resources. To understand factors that influence sharing willingness in evacuations, we employed a multi-modeling approach using three model types: (1) four binary logit models that capture sharing scenario separately; (2) a portfolio choice model (PCM) that estimates dimensional dependency, and (3) a multi-choice latent class choice model (LCCM) that jointly estimates multiple scenarios via latent classes. We tested our approach by employing online survey data from Hurricane Irma (2017) evacuees (n=368). The multi-model approach uncovered behavioral nuances undetectable with one model. For example, the multi-choice LCCM and PCM models uncovered scenario correlation and the multi-choice LCCM found three classes - transportation sharers, adverse sharers, and interested sharers - with different memberships. We suggest that local agencies consider broader sharing mechanisms across resource types and time (i.e. before, during, and after evacuations).

Automated summary

Recent technological advancements have led to the expansion of the sharing economy, coinciding with a growing need for evacuation resources. This study employed a multi-model approach to understand the factors influencing sharing willingness during evacuations. By testing the approach using survey data from Hurricane Irma evacuees, the researchers were able to uncover behavioral nuances that could not be detected with a single model. The findings suggest the importance of considering broader sharing mechanisms across different types of resources and throughout the evacuation process.