Inferring Attribute Non-Attendance Using Eye Tracking in Choice-Based Conjoint Analysis - Narine Yegoryan; Daniel Guhl; Daniel Klapper (Ludwig-Maximilians-Universität München und Humboldt-Universität zu Berlin, Collaborative Research Center Transregio 190 - Rationality and Competition, 2018)
Traditionally, the choice-based conjoint analysis relies on the assumption of rational decision makers that use all available information. However, several studies suggest that people ignore some information when making choices. In this paper, we build upon recent developments in the choice literature and employ a latent class model that simultaneously allows for attribute non-attendance (ANA) and preference heterogeneity. In addition, we relate visual attention derived from eye tracking to the probability of ANA to test, understand, and validate ANA in a marketing context. In two empirical applications, we find that a) our proposed model fits the data best, b) the majority of respondents indeed ignores some attributes, which has implications for willingness-to-pay estimates, segmentation, and targeting, and c) even though the latent class model identifies ANA well without eye tracking information, our model with visual attention helps to better understand ANA by also accounting for differences in attribute processing patterns.
Keywords: 330; attribute non-attendance; eye tracking; discrete choice modeling; choice-based conjoint analysis