How perceived fit affects customers’ satisfaction of in-store social robot advice

In their study "How Perceived Fit Affects Customers’ Satisfaction of In-Store Social Robot Advice", Stephanie van de Sanden, Tibert Verhagen, Ewout Nas, Jacqueline Arnoldy, and Koen Hindriks explore how various dimensions of perceived fit influence customer attitudes and satisfaction toward social robots providing product advice in retail settings. Drawing on theories from marketing and information systems, the authors conceptualize four types of technology fit—task-technology, individual-technology, store-technology, and shopping experience-technology—and propose a model linking these fits to customer attitudes and satisfaction. A field study conducted in a garden center using a robot that advised on potting soil involved 224 participants, whose responses were measured through established Likert and semantic differential scales. The findings aim to inform future design and deployment of social robots in retail by highlighting the importance of contextual and experiential alignment between the robot, task, customer, and environment.