Investigating consumer decision strategies with systems factorial technology

People routinely make multi-attribute decisions about consumer items, such as products and services. The potentially complex decision strategies underlying such consumer decisions have recently been investigated in detail, with most researchers restricting their focus to a relatively small subset of heuristics so as to retain tractability in analyses and identifiability of parameterized cognitive models. Many of these heuristics can be conceived as special cases of a smaller number of overarching dimensions: processing all or a subset of the attribute information describing the consumer options, and processing that attribute information in series or in parallel. These higher-level dimensions correspond to two latent factors of focus in Systems Factorial Technology (SFT), a non-parametric modeling technique that aims to uncover the mental architectures that generate observed decision behavior. Here, we develop a simplified consumer decision scenario and report proof-of-concept evidence regarding the ability of SFT to discriminate between mental architectures, and as a consequence whole classes of decision strategies, in the newly developed consumer task. Our results suggest that most people make decisions prior to processing all available product information. Furthermore, people appear to process numerically presented attribute information in serial, and pictorially presented attribute information in parallel. This extension of SFT beyond its classic domain of application in perceptual processing provides a relatively simple, non-parametric approach to investigating consumer decision strategies.