UCLA

Background: Community organizations active in disasters play a vital role in community disaster response and recovery, but academic understanding of this organizational population is limited by untested and imprecise typological differentiations. An organizational taxonomy would better quantify and define this population and subgroups within it. This would allow for contextualizing research and findings against a validated framework that relates organizational groups and subgroups within the broader population. Taxonomies also serve a role similar to theory by enabling the development of new research questions and hypotheses.

Objectives: This dissertation proposes a taxonomy to classify the organizations of interest, and the taxonomy uses typological benchmarks that ensure coherent classificatory groups to provide meaning-in-context and salience to the needs of its users. The taxonomy evaluated the utility of structural/operational, functional, and financial traits for classifying the organizations. The taxonomy can guide research and policy development, and it can also provide utility to the community organizations themselves and their collaborative networks.

Methods: A novel study population of 660 organizations was created from a stratified non-probability quota sample of 28 Voluntary Organizations Active in Disasters (VOAD) networks. The 660 organizations cover the full range of organizational subtypes/subgroups of interest, and the results are not meant to be generalizable to the VOADs themselves without additional and planned validation. Two sets of hierarchical clustering results were produced and compared using both polythetic and parsimonious trait selection. Iterative and heuristic modeling procedures assessed and compared the results of several important permutations and methodological choices.

Findings: The best set of results classified the population based on a parsimonious set of structural/operational traits: charitable/religious and faith-based/not faith-based. The results from both approaches were robust and congruent with the typological understanding of these organizations, but up to one-fourth of the organizations in the study population exhibited noteworthy deviation from common typological distinctions. The results were expanded into a taxonomy with three branches and three tiers to create a combined total of 26 clusters and subclusters. The best set of results also used a modified unit of analysis that classified 47 coherent organizational subgroups, akin to “species”, rather than 660 individual organizations.