Data-Driven Computational Brand Perception
We introduce a novel approach to predicting and mapping brand trait perceptions using Big Data mined from the Internet combined with machine learning techniques. We show this approach allows us to predict how entire product categories and individual brands are perceived along various trait dimensions by consumers.
Sudeep Bhatia and Christopher Olivola (2018) ,"Data-Driven Computational Brand Perception", in NA - Advances in Consumer Research Volume 46, eds. Andrew Gershoff, Robert Kozinets, and Tiffany White, Duluth, MN : Association for Consumer Research, Pages: 204-208.
Sudeep Bhatia, University of Pennsylvania, USA
Christopher Olivola, Carnegie Mellon University, USA
NA - Advances in Consumer Research Volume 46 | 2018
F8. Dual Routes for Consumer Responses to Corporate Social Responsibility: The Role of Positive Moral Emotions, Attitudes, and Empathy
Chunyan Xie, Western Norway University of Applied Sciences
Richard P. Bagozzi, University of Michigan, USA
Good Gets Better, Bad Gets Worse: The Polarizing Effect of Rating a Consumption Experience
Nahid Ibrahim, University of Alberta, Canada
Gerald Häubl, University of Alberta, Canada
Rory Waisman, University of Alberta, Canada
Running Through Time: How Life Rhythms Foster Identity Permanence
Benjamin Rosenthal, Fundação Getúlio Vargas
Eliane Zamith Brito, Fundação Getúlio Vargas