By Topic

Measuring Affective-Cognitive Experience and Predicting Market Success

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Hyung-il Ahn ; IBM Research-Almaden, San Jose, CA ; Rosalind W. Picard

We present a new affective-behavioral-cognitive (ABC) framework to measure the usual cognitive self-report information and behavioral information, together with affective information while a customer makes repeated selections in a random-outcome two-option decision task to obtain their preferred product. The affective information consists of human-labeled facial expression valence taken from two contexts: one where the facial valence is associated with affective wanting, and the other with affective liking. The new “affective wanting” measure is made by setting up a condition where the person shows desire to receive one of two products, and we measure if the face looks satisfied or disappointed when each of the products arrives. The “affective liking” measure captures facial expressions after sampling a product. The ABC framework is tested in a real-world beverage taste experiment, comparing two similar products that actually went to market, where we know the market outcomes. We find that the affective measure provides significant improvement over the cognitive measure, increasing the discriminability between the two similar products, making it easier to tell which is most preferred using a small number of people. We also find that the new facial valence “affective wanting” measure provides a significant boost in discrimination and accuracy.

Published in:

IEEE Transactions on Affective Computing  (Volume:5 ,  Issue: 2 )