A Multivariate and Latent Class Analysis of Consumer Decision Quality Measures in an E-Service Context

Abstract

Electronic recommendation agents have the potential to be valuable e-service tools in increasing consumer decision quality. Such agents are aimed at assisting consumers in the decision-making process and have the ability to enable consumers to make better choices for themselves. The definition of what constitutes a good choice in this context however remains unclear. Although a variety of approaches have been used to date, there is no consensus on the best measurement approach. This research reviews the various approaches in a variety of disciplines that have been used to define consumer decision quality to date, proposes new measures and a classification typology and examines the relationships among a subset of these measures from an online experimental choice task. Using multivariate and latent class analysis, the results demonstrate how different types of objective and subjective measures provide complementary ways of evaluating decisions and offer useful summaries of decision quality under a variety of conditions. The most important measures and five latent groups of individuals are identified. When individuals do not seem to make good decisions in terms of the individual measures, we propose an explanation of how their decisions are nearly optimal.