This site uses cookies

By using this site, you consent to our use of cookies. You can view our terms and conditions for more information.

The role of decoy effects in nudging preferences for electric vehicles: A novel approach to fuse preference data with and without eye-tracking

Ms. Xinwei Li
National University of Singapore ~ Collage of Civil and Environment
Dr. Prateek Bansal
National University of Singapore ~ Civil & Environmental Engineering

Eye-tracking data such as the gaze patterns reveal important attention-related information about the evidence accumulation process in stated preference (SP) experiments but can only be collected in the lab with a relatively limited number of subjects owing to time and resource constraints. On the other hand, the online SP experiments offer a large sample size but at the expense of eye movement data. Extant literature uses online and lab-based eye-tracking data in isolation. This study develops an approach to elicit consumer preference by jointly leveraging both datasets and validatie it by collecting stated preferences of Singaporean ride-hailing drivers to rent electric vehicles in lab-based (N = 40) and online/street-intercept (N =300) choice experiments. This study is relevant in the local context due to the very high cost of vehicle ownership. To explore the general and accountable interactions between decoy effect strength, attention, and preference formation, an improved Multi-attribute Linear Ballistic Accumulator model concerning absolute attribute value with hierarchical structure (henceforth, HA-MLBA) is adapted and calibrated using both lab-based and online datasets. Specifically, the posterior distribution of HA-MLBA parameters estimated by lab-based data is considered as the prior distribution for the corresponding parameters (including process, alternative-specific parameters, and attribute-specific parameters) while estimating HA-MLBA using online data. To highlight the superiority of this data fusion method, in-sample and out-of-sample performances in fitting choice and response time distribution of the HA-MLBA model with non-informative prior (baseline) and data-fusion prior (informative) are compared. This research demonstrates the presence of the decoy effects (similarity effect and Attraction Effect particularly) in the vehicle rental market. With the increased online purchase of vehicle rentals, such context effects could be vital in nudging ride-hailing drivers to adopt electric vehicles.



Sequential Sampling Model
Process Tracing
Hierarchical Bayesian
Electric vehicles
Data Fusion.

There is nothing here yet. Be the first to create a thread.

Cite this as:

Li, X., & Bansal, P. (2023, July). The role of decoy effects in nudging preferences for electric vehicles: A novel approach to fuse preference data with and without eye-tracking. Paper presented at MathPsych/ICCM/EMPG 2023. Via