Identifying most typical and most ideal attribute levels in small populations of expert decision makers: Studying the Go/No Go decision of disaster relief organizations

TL;DR

This paper introduces a method using Most Typical and Most Ideal attribute levels in ACBC surveys to analyze small populations of expert decision makers, specifically applied to disaster relief organizations' Go/No Go decisions.

Contribution

It proposes a novel approach to identify representative attribute levels in small expert samples using MT and MI concepts within ACBC surveys.

Findings

Effective identification of typical and ideal attribute levels in small samples

Application to disaster relief decision-making scenarios

Enhancement of conjoint analysis for small populations

Abstract

This paper proposes the use of Most Typical (MT) and Most Ideal (MI) levels when an adaptive choice-based conjoint (ACBC) survey can only obtain a small sample size n from a small population size N.