Search Conferences

Type in any word, words or author name. This searchs through the abstract title, keywords and abstract text and authors. You may search all conferences or just select one conference.

 All Conferences
 EMAC 2019 Annual Conference
 EMAC 2020 Annual Conference
 EMAC 2020 Regional Conference

EMAC 2020 Annual Conference

Predicting Adoption Choices Using Choice Probability Elicitation

Published: May 27, 2020


Keyvan Dehmamy, University of Groningen; Thomas Otter, Goethe University; Günter Hitsch, Full Professor of Marketing at University of Chicago; Peter Kurz, bms marketing research + strategy


Conjoint; Probability elicitation; Hierarchical Bayes


Choice-based conjoint analysis is a widely used method to estimate consumer preferences for products and services that are not currently available in the market place from survey responses. In a standard conjoint design consumer report the preferred choice among a set of alternatives. This approach assumes that consumers face no uncertainty about their preferred choice even though the product choice does not occur at the time when the survey is taken but at some future point in time. We propose an alternative design that asks the subjects to state the choice probabilities for each possible product choice. This design allows the subjects to express their subjective uncertainty over the possible choices. Using simulated data, we find that the preference estimates are significantly more precise when obtained using elicited probabilities unless the stated choice probabilities contain much measurement error.