A bounded rationality model of information search and choice in preference measurement
It is becoming increasingly easier for researchers and practitioners to collect eye-tracking data during online preference measurement tasks. The authors develop a dynamic discrete choice model of information search and choice under bounded rationality, which they calibrate using a combination of eye-tracking and choice data. Their model extends Gabaix et al.'s (2006) directed cognition model by capturing fatigue, proximity effects, and imperfect memory encoding and by estimating individual-level parameters and partworths within a likelihood-based hierarchical Bayesian framework. The authors show that modeling eye movements as the outcome of forward-looking utility maximization improves out-of-sample predictions, enables researchers and practitioners to use shorter questionnaires, and allows better discrimination between attributes.
|Keywords||Dynamic discrete choice models, Eye tracking, Preference measurement|
|Persistent URL||dx.doi.org/10.1509/jmr.13.0288, hdl.handle.net/1765/84381|
|Journal||Journal of Marketing Research|
Yang, L, Toubia, O, & de Jong, M.G. (2015). A bounded rationality model of information search and choice in preference measurement. Journal of Marketing Research, 52(2), 166–183. doi:10.1509/jmr.13.0288