In this paper, we investigate how a customer-facing service robot can support decision making in shopping interactions. In this role, a robot needs sometimes to reject a customer’s choice. Thus, we investigate different rejection strategies with the goal of changing customer behavior. The implemented strategies have been developed based on an ethnographic study on assisted shopping and tested in a lab experiment with 31 participants. The experiment showed significant differences in trust ratings and decision-making depending on the employed strategy.

There is a growing interest in the retail industry to deploy service robots for customer interactions. Deploying such customer-facing robots raises the question of how we want to interact with these robots and reveals concerns that businesses and marketers could use robots to manipulate consumers. In this experiment, 67 study participants interacted with different virtual shopping robots that tried to impact "shoppers" purchasing decisions. The results indicate that a robot can increase consumer spending. The study exemplifies how a collaborative robot could be used as a customer-serving robot in a retail environment and investigates the impact of (i) different interaction strategies (human vs robot control) and (ii) dark patterns on shopping behavior (manipulative vs supportive robot).