Using eye-tracking to model attribute non-attendance in choice experiments

ABSTRACT

We investigate revealed attribute attendance in discrete choice experiments using eye-tracking. A simple theoretical framework is proposed in which choices are a function of visual attention. Consistent with the existing literature, the assumption that participants use all the available information to make their decisions does not hold. The results also illustrate that model fit and predictive power are greatly increased by using visual attendance measures as regressors. The use of eye-tracking technology has value for measuring revealed attention and to benchmark with existing choice models.

KEYWORDS:

• Choice experiments
• eye-tracking
• attribute attendance

JEL CLASSIFICATION:

• C91
• C18

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

¹ Unlike logit model specifications where choice probabilities are never zero.

² Eye-tracking augmented models fit and predict better than the original models. Improvements in model fit and prediction are marginal though. Results for non-augmented estimations are available upon request.

³ As a robustness test, different thresholds levels of 10% and 20% were used yielding similar results.