Abstract
Robots that provide customer service in physical stores are being researched as a means of coexisting with people in everyday situations. One issue with such robots is that their suggestions are usually ignored and may not effectively promote purchasing behavior among customers. This paper aims to investigate whether customers are more likely to accept a robot’s suggestion by having the robot use personalized information that can enhance the shopping experience. To investigate the effectiveness of a robot’s suggestions, we conducted an experiment in a physical retail store in a real-world environment. The study aimed to encourage customers to pick up products by utilizing their posture information. As a result, the number of customers who picked up the product increased when the robot made suggestions when customers leaned forward to look at the product. This suggests that using a customer’s posture information to make suggestions can increase the likelihood of a customer accepting a robot’s proposal.
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Acknowledgments
We would like to express our gratitude to everyone at Jintora, a specialized Shichimi shop, for generously providing us with the experimental space. This research project was supported by JSPS KAKENHI Grant Numbers JP19H00605, JP19K21718, JP18KK0053, JP20H01585, JP22K18548 and Artificial Intelligence Research Promotion Foundation.
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Iwasaki, M., Ogawa, K., Kawamura, T., Nakanishi, H. (2023). Gaze-Aware Social Interaction Techniques for Human-Robot Collaborative Shopping. In: Takada, H., Marutschke, D.M., Alvarez, C., Inoue, T., Hayashi, Y., Hernandez-Leo, D. (eds) Collaboration Technologies and Social Computing. CollabTech 2023. Lecture Notes in Computer Science, vol 14199. Springer, Cham. https://doi.org/10.1007/978-3-031-42141-9_16
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