Two-stage discrete choice models for scanner panel data: An assessment of process and assumptions

Discrete choice models such as the multinomial logit assume that consumers choose from the full set of alternatives available to them. However, because (i) consumers may not be able to recall or recognize available brands, (ii) consumers may not have the cognitive capacity or mental energy to process information pertaining to all available brands, or (iii) careful consideration of all available brands might be suboptimal from an economic standpoint given the cost of information search, consumers tend to make choices from a relatively small subset of the available brands. This study assesses the process assumptions of existing two-stage models for scanner panel data and their consistency with the actual processes believed to be used by consumers in forming choice sets. After reviewing what is known from two-stage models in scanner data applications, we highlight issues in need of research.