Can decoy effects nudge ride-hailing drivers’ preferences for electric vehicles?

Abstract

Deploying electric vehicles (EVs) is crucial for achieving ambitious net-zero emission targets. Despite strong demand-side policies, EV adoption remains low, highlighting the need for innovative marketing strategies. We investigate whether the decoy effect can nudge consumer preferences toward EVs. The decoy effect occurs when introducing a new alternative to an existing choice set increases the attractiveness of the target option. Using a street-intercept survey of Singaporean ride-hailing drivers, we show that the decoy effect encourages them to rent EVs over internal combustion engine vehicles, particularly among younger drivers. We also identify the most suitable model to explain the preference shifts induced by the decoy. Our results indicate that the Multi-alternative Decision by Sampling (MDbS) model generally outperforms other sequential sampling and utility-based models under decoy effects. Using MDbS parameters, we estimate the optimal decoy configuration to maximize its influence on preference shifts. Our study presents the first real-world application of the decoy effect in the car rental market, demonstrating its potential to promote EV adoption by integrating MDbS into recommender systems.