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.
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In this paper, we explore the design of web-based advice robots to enhance users' confidence in acting upon the provided advice. Drawing from research on algorithm acceptance and explainable AI, we hypothesise four design principles that may encourage interactivity and exploration, thus fostering users' confidence to act. Through a value-oriented prototype experiment and value-oriented semi-structured interviews, we tested these principles, confirming three of them and identifying an additional principle. The four resulting principles: (1) put context questions and resulting advice on one page and allow live, iterative exploration, (2) use action or change oriented questions to adjust the input parameters, (3) actively offer alternative scenarios based on counterfactuals, and (4) show all options instead of only the recommended one(s), appear to contribute to the values of agency and trust. Our study integrates the Design Science Research approach with a Value Sensitive Design approach.
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To benefit from the social capabilities of a robot math tutor, instead of being distracted by them, a novel approach is needed where the math task and the robot's social behaviors are better intertwined. We present concrete design specifications of how children can practice math via a personal conversation with a social robot and how the robot can scaffold instructions. We evaluated the designs with a three-session experimental user study (n = 130, 8-11 y.o.). Participants got better at math over time when the robot scaffolded instructions. Furthermore, the robot felt more as a friend when it personalized the conversation.
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