Can Decoy Effects Nudge Ride-hailing Drivers’ Preferences for Electric Vehicles?

摘要: In the era of online shopping, incorporating decoy effects in recommender systems could be an effective marketing strategy to nudge consumer preferences. We investigate its potential in promoting electric vehicles (EVs). While testing whether attraction decoys encourage Singaporean ride-hailing drivers to rent an EV, we make three contributions. First, we present a novel discrete choice experiment design with a decoy alternative for real-world applications. Second, we identify an appropriate behavior model to explain the influence of the decoy. We benchmark sequential sampling models (SSMs) against random-utility-maximization-based models using diagrammatic analysis, goodness-of-fit measures, and preference shift measures. Third, we demonstrate how the model parameters can inform the optimal decoy design. Our results show that attraction decoys could nudge ridehailing drivers (especially, younger ones) to rent the target EV. Our extended version of the state-of-the-art SSM, the multi-alternative decision by sampling (MDbS) model, performs the best in explaining such preference shifts. Instead of goodness-of-fit measures, preference shift measures are appropriate to gauge the model’s ability to capture the decoy’s influence. We finally use the parameters of extended MDbS to find the optimal driving range and renting cost of the decoy which could be deployed in recommender systems.