This research takes a first step in modeling latent processes that govern consumer decision making by examining consumption across seemingly disparate categories. Marketing activities today are coordinated in a variety of categories and in a variety of formats, and consumers naturally shop around a globe of unrelated product categories that are beyond the traditionally defined “shopping basket”. We propose a hierarchical multinomial processing tree model to empirically examine the driver, which is defined as the “latent trait”, which governs consumer choices across five seemingly disparate product categories: media consumption, automobile purchases, financial investments, soft drinks and cell phone plans through a dataset consisting of 5,014 consumers in the United States. We further investigate how consumer behavior systematically varies from one category to another and finally suggest new approaches to segment and profile consumers based on latent traits across multiple categories. In doing so, this paper contributes to the consumer decision literature in three ways: 1) theoretically, the latenttrait approach provides rich support in examining the underlying psychological processes; 2) methodologically, the relative merits of models with continuous versus discrete representations of consumer heterogeneity are discussed; and, 3) substantively, new insights on targeting and profiling based on latent processes rather than observed behavior are presented with respect to managing across seemingly unrelated product categories.
|Publication status||Published - Jul 2020|
SourceChina Europe International Business School (CEIBS)
- Seemingly Disparate Categories
- Latent Trait